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France in the Global Economy

Author(s):
Francisco Nadal De Simone, and Alain Kabundi
Published Date:
June 2007
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I. Introduction

Global developments affect the French economy significantly. Standard sources of fluctuations in economic activity include economic developments in trading partners, monetary and exchange rate developments, oil price changes, domestic fiscal policy, ongoing structural reforms, and productivity shocks. Observers of the French economy note that a significant part of fluctuations in French economic activity can be attributed to external sources, though the channels of transmission sometimes defy standard models. For example, French and German consumer confidence indices and French and U.S. business confidence indices exhibit a significant comovement; similarly, there is a strong comovement between the national index of stock prices and the performance of the U.S. economy. Moreover, the role of foreign direct investment (FDI) flows seems sometimes downplayed in empirical work as a relevant additional avenue linking French activity with U.S. activity.

New statistical techniques allow a more reliable extrication of global factors and the identification of the channels via which they interact with the French economy. With recent advances in statistical technology, it has become possible to better assess the sources of comovement of economic activity across countries and the channels of transmission of country- or region-specific shocks. The main reason is that the new models allow the conditions to recover structural shocks to be satisfied more easily, in contrast to the often used small-size structural VARs, where such conditions were unlikely to be met (Hansen and Sargent, 1991; and Fernández-Villaverde and others, 2005). Large dynamic factor models permit the exploitation of the wealth of information included in large panels (Forni, Hallin, Lippi, and Reichlin, 2000; and Kose, Otrok, and Whiteman, 2003; Kapetanios and Marcellino, 2006) and a look inside the “black box” of factor models (Forni, Giannone, Lippi, and Reichlin, 2005; and Eickmeier, 2006). Accordingly, these factors can be related to economically meaningful shocks, and the type of large information sets that economic agents have access to can be taken fully into account. In this vein, two main novel approaches have recently been used: Eickmeier (2005) analyzed the transmission of business cycles from the United States to Germany; and Forni, Giannone, Lippi, and Reichlin (2005) revisited the VAR results of King, Plosser, Stock, and Watson (1991) to identify U.S. shocks on output, consumption and investment.

This paper continues empirical work using factor models and expands it so as to identify the structural shocks that drive French business cycles. Building on previous work using factor models to explain French economic activity and prices (e.g., Nadal De Simone, 2002 and 2005; and Kabundi, 2004), this paper follows Eickmeier’s (2005) framework and uses a sign-restriction strategy to identify the main shocks that affect the French economy and the channels through which it interacts with the global economy. This paper fits in three strands of the literature: first, it relates to the study of the cyclical comovement of activity among countries (e.g., IMF, 2001; and Montfort, Rennee, Rüffer, and Vitale, 2004); second, it is part of studies that explore the channels of transmission of economic shocks across countries (e.g., Kose, Prasad, and Terrones, 2003; and Imbs, 2004); and third, it contributes to the structural VAR literature (Lumsdaine and Prasad, 2003; and Eickmeier and Breitung, 2005) as the structural shocks are identified using that approach.

This study contains three main findings. First, U.S. shocks, especially demand shocks, seem to play an important role in explaining French economic activity, as reflected in the share of the forecast error variance of French variables they account for. Trade in goods and services, relative prices, and FDI flows are the main channels of transmission for all shocks. The stock market and consumer confidence channels seem relatively more relevant for the transmission of U.S. supply shocks, while interest rates seem instead relatively more important for the transmission of demand shocks. Second, indicating France’s increasing regional and global economic integration, the share of French GDP fluctuations explained by the common components has risen over time—a phenomenon also found in Germany. U.S. and G7 (excluding France) economic activity affect French output relatively more via demand shocks while euro area (excluding France) activity affects French output relatively more via supply shocks. Finally, there is some tentative evidence of a possibly small role for regional components, independent of the global common components, in explaining fluctuations in French economic activity. Idiosyncratic components also contribute to the explanation of French output fluctuations. Given the importance of exogenous factors for French economic activity and the fact that France is part of a currency area, French goods, services, and labor markets should be made as flexible as possible. This will reduce income volatility and increase welfare.

The remainder of the paper is organized as follows: Section II discusses the model and the economic conditions for the identification of structural shocks. Section III explains the data, data transformation procedures, and the estimation technique. Section IV discusses the econometric results on the source of the shocks and the channels of transmission. The last section concludes and discusses the policy implications of the paper.

II. Methodology

The methodology used in this paper comprises two main steps. First, estimating the common components of a large panel of data, and second, identifying a reduced number of structural shocks that explain the common components of the variables of interest. In a streamlined way, the estimation procedure requires the following:

  • Use of a large panel of data fulfilling the condition that the number of time series is “much larger” than the number of observations (in a sense to be made clear below).
  • Decompose each time series into two unobserved parts: its common component, driven by shocks common to all series, and its idiosyncratic component.
  • Write the series’ common components as a VAR of low order (often of order one) to represent the reduced form of the model.
  • Estimate the VAR to obtain the coefficients matrix and the reduced-form residuals.
  • Orthogonalize those residuals and obtain the impulse-response functions and forecast error variances.
  • Assume that the orthogonalized residuals are linearly correlated to a vector of “fundamentals” driving the variable of interest via a matrix such that the first shock explains as much as possible of the forecast error variance of the common components; the second one explains as much as possible of the remaining variance, and so on.
  • Concentrate on the first few principal component shocks (neglect others), e.g., the first two principal component shocks.
  • Compute the impulse-response functions and the variance decomposition of the few principal component shocks.
  • Recover the structural shocks that explain the principal component shocks by rotating a matrix such that orthogonal structural shocks produce impulse-responses satisfying a set of economically meaningful (sign) restrictions.
  • Construct confidence intervals for the impulse-responses using bootstrapping so as to account for biases in the VAR coefficients and the agnostic nature of the model.

The estimation procedure is explained in detail below. The reader not interested in technical details can skip the remainder of this section.

A. The Model

This paper uses a large dimensional approximate dynamic factor model. As in Eickmeier (2005), this paper uses the static factor model of Stock and Watson (1998 and 2002). This model is closely related to the traditional factor models of Sargent and Sims (1977) and Geweke (1977), except that it admits the possibility of serial correlation and weakly cross-sectional correlation of idiosyncratic components, as in Chamberlain (1983) and Chamberlain and Rothschild (1983). Similar models have recently been used by Giannone, Reichlin, and Sala (2002); Forni and others (2005); and Eickmeier (2005).

The intuition behind the approximate dynamic factor model analysis is simple. A vector of time series Yt = (y1t, y2t,…, yNt)′ can be represented as the sum of two latent components, a common component Xt = (x1t,x2t,…,xNt)′ and an idiosyncratic component Ξt=(ε1t,ε2t,,εNt)

where Ft = (f1t, f2t,…,frt) is a vector of r common factors, and C =(c1,c2,,cN) is a N ×r matrix of factor loadings, with r <<N. The common component Xt, which is a linear combination of common factors, is driven by few common shocks, which are the same for all variables. Nevertheless, the effects of common shocks differ from one variable to another due to different factor loadings. In this framework and in contrast to standard common component analysis, the idiosyncratic component is driven by idiosyncratic shocks, which are specific to each variable. The static factor model used here differs from the dynamic factor model in that it treats lagged or dynamic factors Ft as additional static factors. Thus, common factors include both lagged and contemporaneous factors.

The identification of the common components requires that the number of series be much larger than the number of observations. Stock and Watson demonstrate that by using the law of large number (as T, N → ∞), the idiosyncratic component, which is weakly correlated by construction, vanishes; and therefore, the common component can be easily estimated in a consistent manner by using standard principal component analysis. The first r eigenvalues and eigenvectors are calculated from the variance-covariance matrix cov(Yt).

and since the factor loadings C =V, equation (1) becomes,

From (1), the idiosyncratic component is

From all the more or less formal criteria to determine the number of static factors r, Bai and Ng (2002) information criteria was followed. As in Forni and others (2005), Ft was approximated by an autoregressive representation of order 13:

where B is a r ×r matrix and ut a r × t vector of residuals. Equation (5) is the reduced form model of (1).

B. Economic Conditions for Shocks Identification

Once a decision is taken on the process followed by the common components, structural shocks have to be identified. The identification of structural shocks is achieved by focusing on the reduced form VAR residuals of (5). Following Eickmeier (2005), the identification scheme has three steps.

First, maximize the variance of the forecast error of the chosen variable and calculate impulse-response functions. As in Uhlig (2003), rather than identifying a shock as, say, a productivity shock, and calculate its contribution to the variance of the k-step ahead prediction error of, say, U.S. GDP, a few major shocks driving GDP are identified.4 This implies maximizing the explanation of the chosen variance of the k-step ahead forecast error of GDP with a reduced number of shocks.5 To this end, k -ahead prediction errors ut are decomposed into k mutually orthogonal innovations using the Cholesky decomposition. The lower triangular Cholesky matrix A is such that ut = Avt and E(vtvt)=I Hence,

The impulse-response function of yit to the identified shock in period k is obtained as follows:

with ci the ith row of factor loadings of C and with a corresponding variance-covariance matrix j=0kRijRij.

Second, the identified shocks are assumed to be linearly correlated to a vector of fundamentals. The fundamental forces ωt = 1t,ω2t,…,ωrt) behind U.S. GDP are correlated to the identified shocks through the r ×r matrix Q. Thus,

The intuition of the procedure is to select Q in such a way that the first shock explains as much as possible of the forecast error variance of the U.S. GDP common component over a certain horizon k, and the second shock explains as much as possible of the remaining forecast error variance. Focusing on the first shock, the task is to explain as much as possible of its error variance

where i is, in our example, the U.S. GDP, and q1 is the first column of Q. The column q1 is selected in such a way that q1σ2q1 is maximized, that is

where Sik=j=0k(k+1j)RijRij.

The maximization problem subject to the side constraint q1 q1=1, can be written as the Lagrangean,

where λ is the Lagrangean multiplier. From (10), q1 is the first eigenvector of Sik with eigenvalue λ and, therefore, the shock associated with q1 is the first principal component shock. Q is the matrix of eigenvectors of S, (q1,q2, …, qr), where ql (l=1,…,r) is the eigenvector corresponding to the lth principal component shock. Along the lines of Uhlig (2003), Eickmeier (2005), and Altig and others (2002), it is posed: k =0 to k =19, i.e., five years, which covers short- as well as medium-run dynamics.

Finally, orthogonal shocks are identified by rotation. If two shocks are identified, following Canova and de Nicoló (2003), the orthogonal shocks vector ωt = 1t,ω2t)’ is multiplied by a 2 ×2 orthogonal rotation matrix P of the form:

where θ is the rotation angle; θ(0,π), produces all possible rotations and varies on a grid. If θ is fixed, and q =5, there are q(q −1)/2 bivariate rotations of different elements of the VAR. Following the insights of Sims (1998), and as in Peersman (2005); Canova and de Nicoló (2003); and Eickmeier (2005); the number of angles between 0 and π is assumed to be 12: this implies 6,191,736,421x1010 (1210) rotations. Hence, the rotated factor wt = Pwt explains in total all the variation measured by the first two eigenvalues. This way, the two principal components ωi are associated to the two structural shocks wi through the matrix P, and the impulse-response functions of the two structural shocks on all the fundamental forces can be estimated.

A sign-identification strategy is followed to identify the shocks. The method was developed by Peersman (2005). This strategy imposes inequality sign restrictions on the impulse response functions of variables based on a typical aggregate demand and aggregate supply framework.6 Only those rotations among all possible q × q rotations that have a structural meaning are chosen. The text table displays the sign restrictions for the identification of shocks that are imposed contemporaneously and during the first year after the shock.7

As in major standard macroeconomic models, a positive supply shock has a nonnegative effect on output and a nonpositive effect on prices during the first four quarters following the shock.8 A positive demand shock has a nonnegative effect on both output and prices during the first four quarters following the shock. A monetary policy tightening has a nonpositive effect on both output and prices during the first four quarters following the shock.

III. Data and Estimation

A. Data Discussion

This paper uses a large data panel. The data panel comprises 482 quarterly series (N = 482) covering the period 1980:Q1–2003:Q4. This implies 96 observations (T = 96). The countries included in the sample are France, Germany, Italy, Japan, Spain, the United Kingdom, and the United States. In addition to national variables, a set of global variables are included, such as a crude oil prices and a commodity industrial inputs price index. The variables cover the real sector of the economy including consumption, investment, international trade in goods and services, portfolio flows and FDI flows, prices, financial variables, and confidence indicators.

For comparison purposes, a shorter time period is also estimated. A data panel for a shorter time period but including the same macroeconomic time series plus a G7 (excluding France) and a euro area (excluding France) real GDP series, and two corresponding price series, is also used (N = 486). This data set covers the period 1991:Q1-2003:Q4, or 51 observations (T = 51). The complete list of variables used in this study is in Appendix I.

Variables were transformed, if necessary, to make them covariance stationary. All the variables are seasonally adjusted. The unit root test developed by Elliot, Rothenberg, and Stock (1996); was applied to all series to decide on the statistical transformation necessary to make them stationary, if needed. The unit root tests included a constant and a deterministic trend. The number of lags was chosen using the Schwarz information criterion and taking care that no serial correlation was left in the residuals. In a few cases, unit root test results were unclear. In those cases, a unit root test with the null hypothesis of stationarity proposed by Kwiatowski, Phillips, Schmidt, and Shin (1992); was used. The statistical treatment of the series is summarized in Appendix I. All series were standardized to have zero mean and unit variance.

B. Estimation

The first step of the estimation is the determination of the number of factors. The estimation was done assuming that the series follow an approximate dynamic factor model.9 As discussed in Section II, the first step is to decide on the number of static factors r making up the common component. Using Bai’s and Ng’s (2002) selection criteria, five factors were retained. Not much can be concluded from the inspection of the factors and their loadings, however, because factors are identified only up to a rotation. Moreover, factors can be a linear combination not only of their contemporaneous values, but also of their lags.

Next, the identification of the structural shocks followed the approach of the structural VAR literature. No identification technology is completely foolproof, however. While the identification technology followed in this paper is flexible enough not to require special restrictions to disentangle common shocks from the contemporaneous transmission of regional or country-specific shocks, it does require additional work, for example, to confirm the source of shocks (e.g., that the shocks originate in the U.S. economy). In order to properly distinguish a global (common) shock from the transmission within the same period of a country- or regional-specific shock, following Eickmeier (2005), this paper does not restrict the impact effect of the shock. Moreover, after identifying two U.S. shocks and giving them an economic interpretation, this study performs the same analysis on a data set containing only U.S. variables. It finds that the impulse-responses of the U.S.-only data set and the broader data set are similar, bringing thus further comfort as to the identification of the source of the shocks. In addition, to test the relative importance of U.S. shocks as sources of disturbances that impact on French activity, the same identification restrictions are imposed on a G7 aggregate of economic activity (excluding France). Finally, the same approach is applied to a euro area aggregate of economic activity (excluding France) to probe the data for what could be a source of “regional” shocks.

Only two structural shocks could be identified. As explained in Section B, the identification procedure proposed by Uhlig (2003) was applied to the common components of U.S. GDP to find a reduced number of structural shocks that maximizes the explanation of its forecast error variance over 20 periods. The procedure was designed to identify three shocks, but could extract two shocks, which suffice to explain 98 percent of the forecast error variance of the common component of U.S. real GDP.

Sign restrictions on impulse response functions were used to provide economic meaning to the structural shocks. Following Peersman (2005), the angle rotations were applied to the first two principal component shocks taking as pairs a supply shock together with a monetary policy shock, a demand shock together with a monetary policy shock, and a supply and a demand shock together. The bootstrap was made up of 500 draws. In the case of the U.S. shocks, only the pair of demand and supply shocks could be identified; no pair containing a monetary policy shock could be identified.10 The same results obtained when identifying G7 and euro area shocks.11 The impulse-response functions are calculated for the first five years to display the cyclical pattern associated with the structural shocks. Both the median response and a 90 percent bootstrapped confidence band are estimated.

IV. Econometric Results

A. U.S. Shocks

In the tradition of the structural VAR literature, results are presented in the form of variance decomposition and impulse-response functions. Table 1 shows the variance shares of the common components of the data set, and the forecast error variance of the common components (henceforth, error variance) of U.S. and French variables explained by the two identified U.S. shocks.12 For comparison purposes, Table 2 displays the error variance of German variables explained by the U.S. shocks. Figure 1 shows the impulse-response functions of the U.S. shocks and their impact on U.S. and French variables.

Figure 1.Impulse-Response Functions

Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Full sample—1980:Q1–2003:Q4 (482 series) Supply Shock (USA)
Identification Inequalities
Positive Supply ShockPositive Demand ShockMonetary Policy Tightening
GDP≥ 0≥ 0≤ 0
Prices≤ 0≥ 0≤ 0
Interest rates≤ 0≥ 0≥ 0
Table 1a.Forecast Error Variance of the Common Components of USA Variables Explained by the USA Supply Shock and the Demand Shock, 1980-2003 1/
Variance Shares

of the Common

Components
Supply

Shocks
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
GDP0.540.870.300.920.110.050.67
Private investment0.620.710.220.850.190.050.58
Personal consumption expenditure0.320.870.400.930.040.020.33
Employment0.600.750.110.820.210.120.83
Productivity0.140.670.210.940.060.010.39
Capacity utilization0.480.120.010.370.610.280.91
Government current disbursements0.580.030.010.570.020.000.21
Government current receipts0.250.340.000.370.390.150.77
Consumer confidence0.660.110.010.320.500.320.91
Business confidence0.740.740.150.860.240.090.79
Consumer prices0.710.240.040.640.460.000.48
Short-term interest rates0.360.150.010.480.830.220.90
Long-term interest rates0.370.020.000.180.950.160.85
M10.440.190.020.380.600.110.81
Stock prices0.090.560.040.750.020.000.25
Wages0.320.310.000.280.420.270.88
Exports total0.380.580.010.650.280.140.88
Imports total0.450.710.220.850.240.060.66
Terms of trade0.130.040.010.470.010.010.50
Real effective exchange0.450.390.000.530.540.000.40
Current account balance0.310.050.000.460.030.010.37
FDI out0.030.040.010.560.260.020.57
FDI in0.000.420.010.500.350.190.86
Table 1b.Forecast Error Variance of the Common Components of France Variables Explained by the USA Supply Shock and the Demand Shock, 1980-2003 1/
Variance Shares

of the Common

Components
Supply

Shock
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
GDP0.430.230.010.300.340.220.85
Private investment0.670.280.010.350.110.080.74
Personal consumption expenditure0.200.400.000.360.020.040.66
Employment0.650.060.010.510.200.050.66
Productivity0.220.600.000.470.110.090.73
Capacity utilization0.570.530.070.720.010.010.32
Government current disbursements0.880.090.000.430.060.000.20
Government current receipts0.730.000.000.460.100.000.29
Consumer confidence0.470.510.120.890.240.010.61
Business confidence0.730.020.010.560.160.060.68
Consumer prices0.840.070.000.450.150.000.22
Short-term interest rates0.200.120.020.540.760.210.88
Long-term interest rates0.310.120.020.470.840.190.88
M1n.a.n.a.n.a.n.a.n.a.n.a.n.a.
Stock prices0.050.570.090.760.040.000.40
Wages0.750.140.040.710.190.000.41
Exports total0.420.030.010.190.890.480.95
Imports total0.370.120.010.280.460.240.86
Terms of trade0.420.290.020.600.690.030.66
Real effective exchange0.180.130.000.330.720.010.69
Current account balance0.030.640.270.860.260.010.53
FDI out0.000.620.030.700.320.210.93
FDI in0.010.150.010.510.750.080.75

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Table 2.Forecast Error Variance of the Common Components of German Variables Explained by the USA Supply Shock and the Demand Shock, 1980-2003 1/
Variance Shares

of the Common

Components
Supply

Shocks
Confidence IntervalsDemand

Shocks
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
GDP0.780.0030.0010.3210.0660.0010.478
Private investment0.570.0390.0020.4220.1100.0010.598
Personal consumption expenditure0.780.0240.0020.3410.0070.0040.273
Employment0.870.1310.0030.4440.0430.0040.302
Productivity0.160.7690.0510.7570.0250.0060.539
Capacity utilizsation0.640.1440.0110.5690.0480.0070.474
Government current disbursements0.830.1930.0040.5240.0090.0190.392
Government current receipts0.760.0820.0030.3710.0300.0050.283
Consumer confidence0.520.1300.0050.4860.0120.0070.536
Business confidence0.620.0570.0050.4400.1460.0350.636
Consumer prices0.560.3610.0030.4980.2010.0010.224
Short-term interest rates0.430.1580.0270.5920.6010.1650.836
Long-term interest rates0.340.0300.0100.3170.8900.3640.926
M1n.a.n.a.n.a.n.a.n.a.n.a.n.a.
Stock prices0.090.5150.0320.6190.2060.0340.645
Wages0.870.1230.0030.5370.0160.0080.286
Exports total0.340.1640.0070.2210.4870.2830.910
Imports total0.280.0660.0050.3300.4990.1450.867
Terms of trade0.570.2870.0090.5610.6700.0190.663
Real effective exchange0.310.3420.0060.5690.6130.0080.585
Current account balancen.a.n.a.n.a.n.a.n.a.n.a.n.a.
FDI out0.010.5940.0990.8150.2560.0050.388
FDI in0.190.3150.0450.5160.4090.0400.698

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

The supply and demand shocks account for 98 percent of the error variance of U.S. GDP common components. When the full sample period, i.e., N = 482 series and T = 95 observations is used, the supply and demand shocks from the United States account for 87 percent and 11 percent of the error variance of U.S. GDP over 20 quarters, respectively. The variance share of U.S. GDP common components is 54 percent.13

The U.S. supply shocks are relatively more important than demand shocks. The relatively larger importance of supply shocks is consistent with the literature on real business cycles that stresses these shocks (i.e., productivity-driven shocks) as the most significant source of U.S. business cycles. Consistently, supply shocks are far more persistent than demand shocks. The results are broadly in agreement with those of Eickmeier (2005).14 Positive demand shocks result in increased investment and consumption, with the rise in the latter relatively less persistent (Figure 1). Following a mild initial increase, productivity declines after a few quarters as the strong effect of the shock on employment is relatively protracted. Given that the measure of capacity utilization used includes new hiring, and that investment, consumption and government net savings increase, demand shocks may be capturing investment-driven cycles (less likely, consumption-driven ones). In the same vein, interest rates rise, especially short-term interest rates, as monetary policy may be trying to offset the effects of the economic expansion on prices as reflected in the CPI. Consistently, the money stock (M1) falls. Finally, and in contrast to supply shocks, demand shocks have virtually no effects on stock prices after 6–8 quarters.

Indirect and direct evidence supports the U.S. origin of the shocks. First, it is noteworthy that the identification strategy followed in this study, by construction, extracts supply and demand shocks that maximize the explained forecast error variance of the common components of U.S. real GDP. Second, indirect and direct evidence suggesting that the source of the identified shocks is the United States is the following. Indirect evidence comes from a dataset containing only U.S. variables. The resulting impulse-response functions were similar to those of the full sample (not shown). Further indirect evidence results from the relatively low values of the common components share of some global variables (i.e., crude oil prices, 26 percent, commodity metal prices, 19 percent, and a commodity industrial input index, 33 percent); it seems unlikely that the identified shocks are global (common) as opposed to U.S.-specific.15 Finally, indirect support for the result that the shocks originate in the United States can be gathered, as discussed below, from the observation that most effects of the U.S. shocks on French variables error variance are significantly smaller than on U.S. variables; given the relatively lower size and larger openness of the French economy, those features of the results are more consistent with a U.S. source than with a global source of the shocks. The direct evidence on the U.S. source of the shocks comes from the estimation of the cross-spectrum of the common components of U.S. and France’s GDP (Figure 2, left side panels). The phase angle is clearly positive in periodicities between 2 and 8 years, the business cycle band, indicating that U.S. GDP common components lead French GDP common components at that frequency band.16

Figure 2.Common Components: Q2 1991 - Q4 2003

Shocks: USA GDP and EU (excluding France) GDP

Source: Staff estimates.

B. Channels of Transmission of U.S. Shocks to France

Broadly speaking, U.S. supply shocks are transmitted to France less forcefully than U.S. demand shocks, and transmission channels go beyond the traditional trade channel. U.S. demand shocks explain over ⅓ of the error variance of French GDP common components while U.S. supply shocks explain less than ¼. The variance shares of French variables suggest that foreign trade and relative prices—i.e., especially terms of trade, and much less so the real exchange rate—matter for the transmission of both U.S. shocks.

However, while U.S. supply shocks explain 3 percent and 12 percent of the error variance of French exports and imports, respectively, demand shocks explain about 90 percent and 45 percent, respectively. In addition, confidence indicators and interest rates variance shares are relatively high. Consumer confidence matters most for the transmission of U.S. supply shocks, while long-term interest rates matter most for the transmission of U.S. demand shocks. It is noteworthy that U.S. demand shocks explain over 80 percent of the error variance of French long-term interest rates, which supports the strong business cycles links between France and the U.S. found in earlier empirical work (Kose and others, 2003; Nadal De Simone, 2003).17 Finally, while admittedly the variance share of the common components of stock prices is relatively low, their error variance following U.S. supply shocks is very large.

U.S. supply shocks seem to be transmitted negatively on French output. While French output seems negatively affected by U.S. supply shocks, with a median error variance of 23 percent over first five years, the outcome for that period is in fact statistically insignificant.18 The large variance share of the current account highlights the role of the trade channel. The current account moves into surplus as, although exports of goods and services fall in the short run, exports increase over time relatively more than imports. The terms of trade improve somewhat, and the real effective exchange rate appreciates marginally, given that the U.S. CPI falls more than the French CPI. While there is no lasting significant change in the real effective exchange, the transient fall in competitiveness magnifies the transmission of U.S. supply shocks. In addition, notice the negative effect on consumption and consumer confidence, consistent with the decline in employment and wages. Stock prices are affected positively and in lasting manner, which mimics their U.S. pattern. The downward impact effect on interest rates (especially short-term interest rates), possibly as a result of an accommodating action on the part of Euro area monetary policy makers, is relatively short-lived. Outward FDI flows are relatively more important than inward FDI flows for the transmission of supply shocks. Given that outward FDI flows decrease and that inward FDI flows increase, the (moderate) negative transmission of U.S. supply shocks to France may be a case of inter-industrial specialization driving trade patterns.19

U.S. demand shocks get transmitted positively to France. Over the sample period, U.S. demand shocks of about 1 percent of GDP (over 20 quarters) have a significant positive impact on France’s real GDP of about 0.5 percent. Exports of goods an services rise more than imports of goods an services in the first 4–6 quarters producing initially a small current account surplus, which turns into a deficit as imports remain high while the impulse on export fades. The terms of trade worsen, most likely due to the effect of the positive U.S. shock on global price variables such as oil and metal prices. The real effective exchange rate depreciates somewhat, especially during the first year, magnifying thereby the U.S. demand shocks’ effects on activity (the counterpart of the U.S. real exchange rate appreciation). There is a lasting, albeit small, positive effect on both consumer and business confidence. Consumption and investment rise in response. Demand drives up French productivity, with benign effects on the price level. Both short- and long-term interest rates increase, most likely as a result of Euro area monetary policy trying to avoid that employment and wage growth translate into inflationary pressures. Stock prices matter relatively little. Finally, in contrast to supply shocks, outward FDI flows are relatively less important than outward FDI flows. In addition, and also in contrast to the effects of U.S. supply shocks, FDI inflows decline, which is difficult to rationalize.

U.S. shocks affect EU member countries asymmetrically.20 A comparison of the variance shares and error variances of French and German variables reveals a few noteworthy points, several of them important to judge the relative flexibility of the two countries’ product and labor markets. First, the variance share of the common components of German GDP is 78 percent against 43 percent in the case of France, a likely outcome of the relatively larger openness of the German economy. However, U.S. shocks affect French output more than German output: U.S. supply and demand shocks affect German GDP less than 1 percent and about 7 percent, respectively, against 23 percent and 34 percent, respectively, in the French case. Second, France responds relatively less to U.S. supply shocks than Germany, at least judging from the relatively lower error variance of prices, employment and productivity, and the real exchange rate. France’s response to U.S. demand shocks is, in contrast, more pronounced than Germany’s. This is illustrated by the relatively high error variance of wages and employment as well as the real exchange rate.21 Third, while the consumer confidence channel seems to matter much more for the transmission of U.S. supply shocks to France than to Germany, stock prices matter more for the transmission of U.S. demand shocks to Germany. Finally, the variance share and the error variance of FDI inflows suggest that they matter relatively more for Germany than for France as channels of transmission of U.S. supply shocks.

C. Is There Evidence of Increasing Interdependence Among Countries?

French interdependence has increased over time. The results of the estimation of the model using the time period 1990:Q1–2003:Q4 show that, as might be expected, France experienced a strengthening of its linkages and interdependence with the rest of the world during the last decade or so. While the total error variance of French GDP explained by U.S. shocks in the full sample period is 57 percent, it increases to 82 percent when the reduced sample period is used (Table 3).22 That increase basically took place through a significant relative rise in the role of U.S. demand shocks. The relative importance of channels of transmission also changed. Besides the enhanced role of the stock market channel in more recent times, confidence channels (notably business confidence) increased their significance.23 Consistently, the impact of investment in explaining activity fluctuations in France also rose, albeit in tandem with the increase in the share of common components in the error variance of French GDP. Finally, it also seems that France’s capacity to adjust to U.S. supply shocks improved somewhat while its capacity to adjust to U.S. demand shocks became more difficult. Note, in particular, the relatively higher (lower) variance of prices that U.S.-driven supply (demand) shocks explain in the reduced sample period. The error variances of the real effective exchange rate display similar changes. Seemingly, the observed increase in the error variances of wages was not sufficient.

Table 3a.Forecast Error Variance of the Common Components of French Variables Explained by the USA Supply Shock and the Demand Shock, 1991-2003 1/
Variance Shares

of the Common

Components
Supply

Shocks
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
GDP0.640.170.010.450.650.170.89
Private investment0.720.360.010.460.370.150.88
Personal consumption expenditure0.270.160.010.670.380.030.86
Employment0.850.480.010.460.210.030.73
Productivity0.420.050.000.470.680.050.82
Capacity Utilisation0.730.380.010.750.070.020.47
Government current disbursements0.630.530.010.680.200.060.88
Government current receipts0.200.420.010.530.460.170.88
Consumer confidence0.710.370.000.470.100.010.58
Business confidence0.740.380.010.390.290.040.76
Consumer prices0.320.350.000.620.070.010.65
Short-term interest rates0.460.070.010.460.190.020.56
Long-term interest rates0.750.030.000.470.220.020.74
M1n.a.n.a.n.a.n.a.n.a.n.a.n.a.
Stock prices0.220.580.010.590.170.010.56
Wages0.630.200.010.530.320.020.71
Exports total0.500.160.010.370.470.100.78
Imports total0.500.370.010.460.500.280.90
Terms of trade0.330.060.010.490.090.010.39
Real effective exchange0.230.310.010.480.280.010.53
Current account balance0.120.040.000.640.280.000.41
FDI out0.010.090.010.740.750.080.91
FDI in0.020.070.000.490.060.010.36

Adjustment to U.S. shocks varies across countries. When France is compared with Germany, a few points merit stressing. First, it is noticeable that the error variance of French price variables is in general lower than German variables following U.S. (especially supply) shocks (e.g., compare the error variances of prices, wages and the real exchange rate on Table 3a for France and on Table 3b for Germany).24 Consistently, employment does relatively more of the adjustment to U.S. supply shocks in France than in Germany. Second, the adjustment via short-term interest rates following U.S. demand shocks is more significant for Germany than for France. Finally, confidence channels matter for U.S. supply shocks relatively more in France and for U.S. demand shocks relatively more in Germany.

Table 3b.Forecast Error Variance of the Common Components of German Variables Explained by the USA Supply Shock and the Demand Shock, 1991-2003 1/
Variance Shares

of the Common

Components
Supply

Shocks
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
GDP0.420.150.010.600.810.220.97
Private investment0.370.160.010.560.810.220.93
Personal consumption expenditure0.210.160.000.750.600.010.80
Employment0.630.590.000.510.160.030.76
Productivity0.420.120.010.610.800.050.83
Capacity utilization0.800.300.000.420.110.010.69
Government current disbursements0.610.520.000.580.000.000.47
Government current receipts0.560.270.000.620.290.010.40
Consumer confidence0.640.190.010.590.310.020.69
Business confidence0.700.170.010.510.570.050.83
Consumer prices0.570.370.000.570.010.010.62
Short-term interest rates0.550.090.010.600.530.030.79
Long-term interest rates0.370.020.000.470.210.010.74
M1n.a.n.a.n.a.n.a.n.a.n.a.n.a.
Stock prices0.300.560.010.590.250.010.67
Wages0.630.290.010.820.330.000.57
Exports total0.390.440.010.510.300.090.83
Imports total0.390.450.010.540.460.220.91
Terms of trade0.240.140.010.460.190.020.63
Real effective exchange0.150.470.010.540.210.030.79
Current account balancen.a.n.a.n.a.n.a.n.a.n.a.n.a.
FDI out0.010.220.010.650.060.020.40
FDI in0.230.310.010.410.240.020.63

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

The predominant role played by U.S. shocks is also clear in the shorter sample period. With data available for 1991:Q1–2003:Q4 for broader aggregates of global and regional economic activity, the paramount role of U.S. shocks seems confirmed. When the shock is to G7 economic activity (excluding France), the error variance of French GDP explained increases to 82 percent (25 percentage points more than when shocks are from the United States, in the period 1980–2003). These results further stress the large role played by U.S. shocks in international business cycles.

There is limited evidence of relatively minor “regional shocks.” When the shock is to the euro area activity measure (excluding France), the error variance of French GDP explained also rises to 64 percent (Table 4). The cross-spectrum of EU and French GDP common components is broadly similar to the one of U.S. and French GDP common components (Figure 2), with one important caveat: only EU GDP common components lead France’s common components in the very long run. In addition, the cross-spectrum of U.S. and EU GDP common components shows that the U.S. leads the EU (Figure 3) in periodicities ranging between 7 and 128 quarters. The results suggest there may be some role for “regional factors” in explaining the error variance of French GDP, but that role can be tentatively considered small. This finding is broadly consistent with several studies pointing to a relatively minor role to regional factors (e.g., Kose, Otrok, and Whiteman, 2003; and Nadal De Simone, 2003). Summarizing all cross-spectrum results, the analysis indicates: (1) only the U.S. leads France in periodicities ranging between 8 quarters and 15 quarters; (2) the EU and the U.S. together lead France in periodicities ranging between 16 and 128 quarters and;(3) the EU and France comove in the very long run.

Figure 3.Common Components: Q2 1991 -Q4 2003

Shocks: USA GDP and EU (excluding France) GDP

Source: Staff estimates.

Table 4a.Forecast Error Variance of the Common Components of French Variables Explained by the G7 Excluding France Supply Shock and the Demand Shock, 1991-2003 1/
Variance Shares

of the Common

Components
Supply

Shock
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
GDP0.640.110.010.350.810.410.96
Private investment0.720.330.010.520.430.170.90
Personal consumption expenditure0.270.180.010.440.310.070.80
Employment0.850.470.010.610.280.030.74
Productivity0.420.150.010.410.790.160.91
Capacity utilization0.730.320.030.730.090.010.37
Government current disbursements0.630.590.010.790.160.030.77
Government current receipts0.200.340.010.600.550.110.85
Consumer confidence0.710.380.010.540.180.010.60
Business confidence0.740.320.010.490.460.090.81
Consumer prices0.320.520.000.710.000.000.39
Short-term interest rates0.460.090.010.390.570.070.72
Long-term interest rates0.750.090.000.390.580.190.89
M1n.a.n.a.n.a.n.a.n.a.n.a.n.a.
Stock prices0.220.580.010.700.150.000.34
Wages0.630.160.020.410.520.070.79
Exports total0.500.060.010.320.830.320.90
Imports total0.500.270.010.550.690.350.95
Terms of trade0.330.020.000.380.430.010.55
Real effective exchange0.230.200.010.530.480.010.51
Current account balance0.120.080.000.530.030.000.43
FDI out0.010.070.010.570.560.090.83
FDI in0.020.230.000.430.300.010.58
Table 4b.Forecast Error Variance of the Common Components of French Variables Explained by the Euro Area Excluding France Supply Shock and the Demand Shock, 1991-2003 1/
Variance Shares

of the Common

Components
Supply

Shock
Confidence IntervalsDemand

Shock
Confidence Intervals
Lower BoundUpper BoundLower BoundUpper Bound
GDP0.640.770.090.910.210.050.88
Private investment0.720.800.120.920.040.020.74
Personal consumption expenditure0.270.530.010.780.070.030.82
Employment0.850.800.070.880.040.010.62
Productivity0.420.200.000.480.650.120.91
Capacity utilization0.730.260.050.500.150.010.52
Government current disbursements0.630.670.150.930.100.010.52
Government current receipts0.200.930.080.910.030.020.74
Consumer confidence0.710.610.040.780.040.010.58
Business confidence0.740.840.080.880.040.020.72
Consumer prices0.320.300.010.750.190.000.39
Short-term interest rates0.460.320.020.640.320.030.69
Long-term interest rates0.750.170.010.720.340.010.65
M1n.a.n.a.n.a.n.a.n.a.n.a.n.a.
Stock prices0.220.670.010.700.090.000.36
Wages0.630.660.030.760.140.030.80
Exports total0.500.660.050.770.190.040.77
Imports total0.500.930.250.950.060.020.73
Terms of trade0.330.240.010.560.140.010.45
Real effective exchange0.230.740.010.710.030.010.56
Current account balance0.120.110.010.590.000.000.36
FDI out0.010.410.020.650.130.030.62
FDI in0.020.030.010.420.380.010.59

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Asymmetries in business cycle transmission persist during the shorter sample period. U.S. and G7 economic activity affect French output relatively more via demand shocks, while euro area activity affects French output relatively more via supply shocks. This is likely the outcome of the relatively richer vertical and horizontal integration between French and regional firms than between French and G7 firms—other than euro area. As an illustration, the supply shocks from the euro area aggregate explain a significantly larger share of the error variance of exports of goods and services than the G7 shocks or the U.S. shocks (i.e., 66 percent versus 6 percent and 16 percent, respectively). Similarly, the large increase in the error variance of French confidence variables (especially business confidence) when the shock is to euro area activity, further indicates the likely presence of a regional factor which, albeit seemingly small, deserves further analysis.

V. Conclusion and Policy Implications

While certainty about the sources of shocks is not easily achievable, there is strong evidence that French output behavior is significantly affected by U.S. shocks. This study found that U.S. shocks, especially demand shocks, seem to play an important role in explaining the behavior of French economic activity. International trade in goods and services, the terms of trade, the real effective exchange rate, and FDI flows are the main channels of transmission of U.S. demand and supply shocks. Financial variables, such as interest rates, are also important. The stock market and consumer confidence channels seem relatively more relevant for the transmission of U.S. supply shocks, with interest rates instead being relatively more important for the transmission of demand shocks. There still remains a significant role for idiosyncratic components to contribute to the explanation of French output fluctuations, but relatively less than in the German case, especially when the period considered excludes the 1980s. This indicates that French economic policies do matter.

France has become more integrated to the world economy over time. The interdependence of the French economy has increased over time, and the role of financial variables as channels of transmission of shocks has become relatively more important. The increased importance of the business confidence channel is also noteworthy (at least judging from the increase in the variance share of the common components). In addition, and compared to Germany, the French economy reacts (especially) to U.S. supply shocks relying relatively more on employment and real exchange rate changes than on price changes.

U.S. shocks explain a larger part of French output common components than a broader aggregate of economic activity. While the use of a broader aggregate of economic activity than just U.S. real GDP increases the importance of the common components in explaining French economic activity fluctuations, the bulk of output variance can already be captured by a pair of distinctively U.S. shocks. This seems especially the case for the post-1990 period. The results stress the important role played by fluctuations in U.S. economic activity in explaining French economic fluctuations.

However, given that idiosyncratic components do matter in explaining French output fluctuations, the French economy would benefit from further structural reforms that increase its flexibility. The importance of trade flows and relative price changes in the international transmission of disturbances highlights the relevance of domestic price flexibility. As the results of the paper suggest, following U.S. supply shocks, the speed of adjustment of French prices relative to U.S. prices is lower. This will matter for the magnitude of the real effective exchange rate changes, trade flows, and the size of the current account balance that will be necessary to accommodate the given disturbance. Similarly, following shocks in the United States, it is likely that, ceteris paribus, the level of interest rates consistent with macroeconomic stability in France will be higher the less flexible the economy is; this seems to be the case given the larger variance share of long-term interest rates in France than in Germany. These conclusions are hardly unexpected, but the framework used in this paper has evinced, in a robust way, their policy relevance.

The asymmetry in the transmission of U.S. shocks to EU members further supports calls to increase market’s flexibility. The asymmetry in the transmission of shocks across countries— illustrated here by comparing French and German variables’ responses to U.S. shocks— together with the predominant role that exogenous factors play in the dynamics of French output, argue for domestic policies geared toward boosting goods, services, and labor markets flexibility in France.

Acronyms

CUCapacity utilization
GDGovernment current disbursements
GRGovernment current receipts
GSGovernment net savings
C ConfidenceConsumer confidence
B ConfidenceBusiness confidence
CPIConsumer price index
ST IntShort-term interest rate
LT IntLong-term interest rate on government bonds
SPShare price index
TTTerms of trade
REERReal effective exchange rate
CACurrent account of the balance of payments
FDIForeign direct investment flows
APPENDIX I. Macroeconomic Series
NumberCountryVariable NameUnit RootLogTreatment
1FranceBalance of income, value, balance of payments basis1n12
2FranceCurrent account, value1n12
3FranceGovernment consumption of fixed capital, value113
4FrancePrivate final consumption expenditure, volume\euros 1995113
5FranceDependent employment\persons113
6FranceDependent employment of the business sector\persons113
7FranceGovernment employment\persons113
8FranceSelf-employed\persons113
9FranceTotal employment\persons113
10FranceExchange rate, index of US$ per local currency\index113
11FranceEmployment of the business sector\persons113
12FranceReal Effective exchange rate, 2000 = 100, ULC-based113
13FranceGross domestic product, volume, market prices\euros 1995113
14FrancePrivate nonresidential fixed capital formation, volume\euros 1995113
15FranceFixed investment in nonresidential construction, volume113
16FranceGovernment fixed capital formation, volume\euros 1995113
17FrancePrivate residential fixed capital formation, volume\euros 1995113
18FranceFixed investment in machinery and equipment, volume\euros113
19FranceIndustrial production\index 1995113
20FrancePrivate total fixed capital formation, volume\euros 1995113
21FranceLong-term interest rate on government bonds\percent1n12
22FranceGross total fixed capital formation, volume\euros 1995113
23FranceLabor force\persons113
24FranceLabor force participation rate113
25FranceImports of goods and services, volume, national accounts basis\euros113
26FranceFactor income paid abroad, volume, balance of payments basis\local currency113
27FranceLabor productivity of the total economy\index 2000113
28FranceLabor productivity of the business economy\euros113
29FranceGovernment saving (net), value\euros1n12
30FranceHousehold saving ratio\percent1n12
31FranceCurrent transfers received by households, value\euros113
32FranceUnit labor cost of the total economy\index 2000113
33FranceUnit labor cost of the manufacturing sector\index 1995113
34FranceUnemployment\persons113
35FranceUnemployment rate\percent1n12
36FranceWages, value\euros113
37FranceWages of the government sector, value\euros113
38FranceCompensation rate of government employees\euros113
39FranceWage rate of the manufacturing sector, hourly earnings\index 19951n12
40FranceCompensation rate of the business sector\yearly salary in euro113
41FranceCompensation of employees, value\euros113
42FranceExports of goods and services, volume, national accounts basis\euros 1995113
43FranceFactor income from abroad, volume, balance of payments basis\local currency113
44FranceProperty income received by households, value\euros113
45FranceGovernment current disbursements, value\euros113
46FranceCurrent disbursements of households, value\euros113
47FranceGovernment current receipts, value\euros113
48FranceCurrent receipts of households, value\euros113
49FranceSelf-employment income received by households, value\euros113
50FranceDirect Investment abroad1n12
51FranceDir. invest. in rep. econ., N.I.E.1n12
52FrancePortfolio investment liab., N.I.E.1n12
53FranceExports prices113
54FranceImports prices113
55FranceTerms of trade113
56FranceCPI: 108 cities (index number, 2000=100, AQM, DEC, average)113
57FranceFrance\interest rates\confidence and economic sentiment\share prices SBF 250/stock113
58FranceTreasury bills: 3 months (percent per annum, AQM, DEC, average)1n12
59FranceCyclical indicators\surveys of manufacturing industry:\industrial confidence indicator0n10
60France\Cyclical indicators\consumer opinion on economic and financial0n10
61FranceFixed investment in construction, volume011
62FranceIncrease in stocks, volume\euros 19950n10
63FranceWage rate of the business sector\euros per011
64FranceHousehold disposable income, real\euros011
65FranceFrance\cyclical indicators\surveys of manufacturing industry:\current level of capacity011
66FrancePortfolio investment assets0n10
67FranceOther investment assets0n10
68FranceOther investment liab., N.I.E.0n10
69FranceFinancial account, N.I.E.0n10
70GermanyGovernment consumption of fixed capital, value\euros113
71GermanyPrivate final consumption expenditure, volume\euros 1995113
72GermanyDependent employment\persons113
73GermanyDependent employment of the business sector113
74GermanyGovernment employment\persons113
75GermanySelf-employed\persons113
76GermanyTotal employment\persons113
77GermanyEmployment of the business sector113
78GermanyExchange rate, index of US$ per local currency\index113
79GermanyReal Effective exchange rate, 2000 = 100, ULC-based113
80GermanyGross domestic product, volume, market prices\euros 1995113
81GermanyPrivate nonresidential fixed capital formation, volume\euros 1995113
82GermanyFixed investment in nonresidential construction, volume113
83GermanyFixed investment in construction, volume\DM113
84GermanyGovernment fixed capital formation, volume\euros 1995113
85GermanyPrivate residential fixed capital formation, volume\euros 1995113
86GermanyFixed investment in machinery and equipment, volume\DM113
87GermanyIndustrial production113
88GermanyPrivate total fixed capital formation, volume\euros 1995113
89GermanyLong-term interest rate on government bonds\percent1n12
90GermanyGross total fixed capital formation, volume\euros 1995113
91GermanyLabor force113
92GermanyImports of goods and services, volume, national accounts basis\euros 1995113
93GermanyLabor productivity of the total economy\index 2000113
94GermanyLabor productivity of the business economy113
95GermanyGovernment saving (net), value\euros1n12
96GermanyCurrent transfers received by households, value113
97GermanyUnit labor cost of the total economy113
98GermanyUnit labor cost of the manufacturing sector\Local currency index113
99GermanyUnemployment\euros113
100GermanyUnemployment rate\percent1n12
101GermanyWages, value\euros113
102GermanyWage rate of the business sector113
103GermanyCompensation rate of government employees113
104GermanyCompensation rate of the business sector\DM113
105GermanyCompensation of employees, value\euros113
106GermanyExports of goods and services, volume, national accounts basis\euros 1995113
107GermanyHousehold disposable income, real\euros113
108GermanyGovernment current disbursements, value\euros113
109GermanyCurrent disbursements of households, value\euros113
110GermanyGovernment current receipts, value\euros113
111GermanyCurrent receipts of households, value\euros113
112GermanyDirect Investment abroad1n12
113GermanyPortfolio investment assets1n12
114GermanyPortfolio investment liab., N.I.E.1n12
115GermanyExports prices113
116GermanyImports prices113
117GermanyTerms of trade113
118GermanyShare prices (Index number, AQM, DEC, average)113
119GermanyCall money rate (percent per annum, AQM, DEC, average)1n12
120GermanyConsumer Price Index (SA, 2000=100)113
121GermanyPPI: total manufacturing industries (SA, 2000=100)113
122GermanyCyclical indicators\surveys of manufacturing industry:\industrial confidence indicator0n10
123GermanyCyclical indicators\consumer opinion on economic and financial0n10
124GermanyIncrease in stocks, volume\euros 19950n10
125GermanyHousehold saving ratio\percent0n10
126GermanyThe Federal Republic of Germany (prior to 1990Q4 West-Germany)\cyclical011
127GermanyDir. Invest. in Rep. Econ., N.I.E.0n10
128GermanyOther investment assets0n10
129GermanyOther investment liab., N.I.E.0n10
130GermanyFinancial account, N.I.E.0n10
131ItalyBalance of income, value, balance of payments basis1n12
132ItalyCurrent account, value1n12
133ItalyGovernment consumption of fixed capital, value\euros113
134ItalyPrivate final consumption expenditure, volume\euros 1995113
135ItalyDependent employment\persons113
136ItalySelf-employed\persons113
137ItalyTotal employment\persons113
138ItalyEmployment of the business sector\persons113
139ItalyExchange rate, index of US$ per local currency\index113
140ItalyPrivate non-residential fixed capital formation, volume\euros113
141ItalyFixed investment in non-residential construction, volume\euros113
142ItalyFixed investment in construction, volume\euros113
143ItalyGovernment fixed capital formation, volume\euros113
144ItalyPrivate residential fixed capital formation, volume\euros113
145ItalyFixed investment in machinery and equipment, volume\euros113
146ItalyIndustrial production\index 1995113
147ItalyPrivate total fixed capital formation, volume\euros113
148ItalyLong-term interest rate on government bonds\percent1n12
149ItalyGross total fixed capital formation, volume\euros113
150ItalyCapital stock of the business sector, volume\euros113
151ItalyCapital stock, housing, volume113
152ItalyLabor force\persons113
153ItalyLabor force participation rate1n12
154ItalyImports of goods and services, volume, national accounts basis\euros113
155ItalyFactor income paid abroad, volume, balance of payments basis\local currency113
156ItalyLabor productivity of the total economy\index 2000113
157ItalyLabor productivity of the business economy\euros113
158ItalyGovernment saving (net), value\euros1n12
159ItalyHousehold saving, value\euros113
160ItalyHousehold saving ratio\percent1n12
161ItalyCurrent transfers received by households, value\euros113
162ItalyUnit labor cost of the total economy\local currency113
163ItalyUnit labor cost of the manufacturing sector\local currency index113
164ItalyUnemployment\persons113
165ItalyUnemployment rate\percent1n12
166ItalyWages, value\euros113
167ItalyWage rate of the business sector\euros/person113
168ItalyCompensation rate of government employees\euros/person113
169ItalyWage rate of the manufacturing sector, hourly earnings\index 1995113
170ItalyCompensation rate of the business sector\yearly salary in euros per113
171ItalyCompensation of employees, value\euros113
172ItalyExports of goods and services, volume, national accounts basis\euros113
173ItalyFactor income from abroad, volume, balance of payments basis\local currency113
174ItalyHousehold disposable income, real\euros113
175ItalyProperty income received by households, value\euros113
176ItalyGovernment current disbursements, value\euros113
177ItalyCurrent disbursements of households, value\euros113
178ItalyGovernment current receipts, value\euros113
179ItalyCurrent receipts of households, value\euros113
180ItalySelf-employment income received by households, value\euros113
181ItalyPortfolio investment liab., N.I.E.1n12
182ItalyExports prices113
183ItalyImports prices113
184ItalyTerms of trade113
185ItalyCPI: all Italy (index number, 2000=100, AQM, DEC, average)113
186ItalyItaly\interest rates\confidence and economic sentiment\share prices ISE MIB113
187ItalyMoney market rate (percent per annum, AQM, DEC, average)1n12
188ItalyReal Effective exchange rate, 2000 = 100, ULC-based011
189ItalyGross domestic product, volume, market prices\EUROS 1995011
190ItalyIncrease in stocks, volume\EUROS0n10
191ItalyItaly\cyclical indicators\surveys of manufacturing industry:\current level of capacity011
192ItalyDirect investment abroad0n10
193ItalyDir. invest. in rep. econ., N.I.E.0n10
194ItalyPortfolio investment assets0n10
195ItalyOther investment assets0n10
196ItalyOther investment liab., N.I.E.0n10
197ItalyFinancial account, N.I.E.0n10
198JapanBalance of income, value, balance of payments basis1n12
199JapanCurrent account, value1n12
200JapanGovernment consumption of fixed capital, value\JPY113
201JapanPrivate final consumption expenditure, volume\JPY 2000113
202JapanDependent employment\persons113
203JapanDependent employment of the business sector\persons113
204JapanGovernment employment\persons113
205JapanSelf-employed\persons113
206JapanTotal employment\persons113
207JapanEmployment of the business sector\persons113
208JapanExchange rate, index of US$ per local currency\index113
209JapanReal Effective exchange rate, 2000 = 100, ULC-based113
210JapanGross domestic product, volume, market prices\JPY 2000113
211JapanPrivate non-residential fixed capital formation, volume\JPY 2000113
212JapanFixed investment of government enterprises, volume\JPY 2000113
213JapanGovernment fixed capital formation, volume\JPY 2000113
214JapanPrivate residential fixed capital formation, volume\JPY 2000113
215JapanIndustrial production\index 2000113
216JapanPrivate total fixed capital formation, volume\JPY 2000113
217JapanLong-term interest rate on government bonds\percent1n12
218JapanGross total fixed capital formation, volume\JPY 2000113
219JapanCapital stock of the business sector, volume\JPY 2000113
220JapanCapital stock, housing, volume\JPY 2000113
221JapanLabor force\persons113
222JapanLabor force participation rate1n12
223JapanImports of goods and services, volume, national accounts basis\JPY 2000113
224JapanMoney supply, broad definition: M2 or M3\JPY113
225JapanFactor income paid abroad, volume, balance of payments basis\local currency113
226JapanLabor productivity of the total economy\index 2000113
227JapanLabor productivity of the business economy113
228JapanGovernment saving (net), value\JPY1n12
229JapanHousehold saving, value\JPY113
230JapanHousehold saving ratio\percent1n12
231JapanUnit labor cost of the total economy\index 2000113
232JapanUnit labor cost of the manufacturing sector\index 2000113
233JapanUnemployment\persons113
234JapanUnemployment rate\percent1n12
235JapanVelocity of money113
236JapanWages, value\JPY113
237JapanWage rate of the business sector\index113
238JapanCompensation rate of government employees113
239JapanWage rate of the manufacturing sector, hourly earnings\index 2000113
240JapanCompensation rate of the business sector\yearly salary in yen per113
241JapanCompensation of employees, value\JPY113
242JapanExports of goods and services, volume, national accounts basis\JPY 2000113
243JapanFactor income from abroad, volume, balance of payments basis\local currency113
244JapanHousehold disposable income, real\JPY113
245JapanProperty income received by households, value\JPY113
246JapanGovernment current disbursements, value\JPY113
247JapanCurrent disbursements of households, value\JPY113
248JapanGovernment current receipts, value\JPY113
249JapanCurrent receipts of households, value\JPY113
250JapanSelf-employment income received by households, value\JPY113
251JapanDirect Investment abroad1n12
252JapanPortfolio investment assets1n12
253JapanFinancial account, N.I.E.1n12
254JapanExports prices113
255JapanImports prices113
256JapanTerms of trade113
257JapanCall monetary rate (percent per annum, AQM, DEC, average)1n12
258JapanShare prices (index number, AQM, DEC, average)113
259JapanPPI/WPI (Index number, 2000=100, AQM, DEC, average)113
260JapanCPI: all Japan-485 items (Index number, 2000=100, AQM, DEC, average)113
261JapanIncrease in stocks, volume\JPY 20000n10
262JapanCurrent transfers received by households, value\JPY011
263JapanDir. invest. in rep. econ., N.I.E.0n10
264JapanPortfolio investment liab., N.I.E.0n10
265JapanOther investment liab., N.I.E.0n10
266SpainBalance of income, value, balance of payments basis1n12
267SpainCurrent account, value1n12
268SpainGovernment consumption of fixed capital, value\euros113
269SpainUnit capital-labor costs113
270SpainPrivate final consumption expenditure, volume\euros113
271SpainDependent employment\persons113
272SpainDependent employment of the business sector\persons113
273SpainGovernment employment\persons113
274SpainSelf-employed\persons113
275SpainTotal employment\persons113
276SpainEmployment of the business sector\persons113
277SpainExchange rate, index of US$ per local currency\index113
278SpainReal Effective exchange rate, 2000 = 100, ULC-based113
279SpainGross domestic product, volume, market prices\euros113
280SpainPrivate non-residential fixed capital formation, volume\euros113
281SpainFixed investment in non-residential construction, volume\euros113
282SpainFixed investment in construction, volume113
283SpainGovernment fixed capital formation, volume\euros113
284SpainPrivate residential fixed capital formation, volume\euros113
285SpainFixed investment in machinery and equipment, volume\euros113
286SpainIndustrial production\index113
287SpainPrivate total fixed capital formation, volume\euros113
288SpainLong-term interest rate on government bonds\percent1n12
289SpainGross total fixed capital formation, volume\euros113
290SpainLabor force\persons113
291SpainImports of goods and services, volume, national accounts basis\euros113
292SpainFactor income paid abroad, volume, balance of payments basis\local currency113
293SpainLabor productivity of the total economy\index113
294SpainLabor productivity of the business economy\euros113
295SpainGovernment saving (net), value\euros1n12
296SpainHousehold saving, value\euros113
297SpainCurrent transfers received by households, value\euros113
298SpainUnit labor cost of the total economy\index113
299SpainUnit labor cost of the manufacturing sector\index113
300SpainUnemployment\persons113
301SpainUnemployment rate\percent1n12
302SpainWages, value\euros113
303SpainWage rate of the business sector\euros/man/year113
304SpainCompensation rate of government employees\euros113
305SpainCompensation rate of the business sector\yearly salary in euros113
306SpainCompensation of employees, value\euros113
307SpainExports of goods and services, volume, national accounts basis\euros113
308SpainFactor income from abroad, volume, balance of payments basis\local currency113
309SpainHousehold disposable income, real\euros113
310SpainProperty income received by households, value\euros113
311SpainGovernment current disbursements, value\euros113
312SpainCurrent disbursements of households, value\euros113
313SpainGovernment current receipts, value\euros113
314SpainCurrent receipts of households, value\euros113
315SpainSelf-employment income received by households, value\euros113
316SpainOther investment liab., N.I.E.1n12
317SpainExports Prices113
318SpainTerms of Trade113
319SpainCall money rate (percent per annum, AQM, DEC, average)1n12
320SpainShare prices (index number, AQM, DEC, average)113
321SpainPPI/WPI (index number, 2000=100, AQM, DEC, average)113
322SpainCPI: (no specifics avail.) (index number, 2000=100, AQM, DEC, average)113
323SpainIncrease in stocks, volume\euros0n10
324SpainHousehold saving ratio\ratio0n10
325SpainDirect investment abroad0n10
326SpainDir. Invest. in rep. econ., N.I.E.0n10
327SpainPortfolio investment liab., N.I.E.0n10
328SpainOther investment assets0n10
329SpainFinancial account, N.I.E.0n10
330SpainImports Prices011
331United KingdomBalance of income, value, balance of payments basis1n12
332United KingdomCurrent account, value1n12
333United KingdomGovernment consumption of fixed capital, value\GBP113
334United KingdomUnit capital-labor costs113
335United KingdomPrivate final consumption expenditure, volume\2001 GBP113
336United KingdomDependent employment\persons113
337United KingdomDependent employment of the business sector\persons113
338United KingdomGovernment employment\persons113
339United KingdomSelf-employed\persons113
340United KingdomTotal employment\persons113
341United KingdomEmployment of the business sector\persons113
342United KingdomExchange rate, index of US$ per local currency\index113
343United KingdomReal Effective exchange rate, 2000 = 100, ULC-based113
344United KingdomGross domestic product, volume, market prices\2001 GBP113
345United KingdomPrivate non-residential fixed capital formation, volume\GBP113
346United KingdomFixed investment in construction, volume\GBP 2001113
347United KingdomGovernment fixed capital formation, volume\GBP 00113
348United KingdomPrivate residential fixed capital formation, volume\2001 GBP113
349United KingdomFixed investment in machinery and equipment, volume\GBP 2001113
350United KingdomPrivate total fixed capital formation, volume\GBP 00113
351United KingdomLong-term interest rate on government bonds\percent1n12
352United KingdomIncrease in stocks, volume\2001 GBP1n12
353United KingdomGross total fixed capital formation, volume\2001 GBP113
354United KingdomCapital stock of the business sector, volume\GBP 2001113
355United KingdomLabor force\persons113
356United KingdomLabor force participation rate1n12
357United KingdomImports of goods and services, volume, national accounts basis\GBP 2001113
358United KingdomFactor income paid abroad, volume, balance of payments basis\GBP113
359United KingdomLabor productivity of the total economy\index 2000113
360United KingdomLabor productivity of the business economy113
361United KingdomHousehold saving, value\GBP113
362United KingdomHousehold saving ratio\percent1n12
363United KingdomCurrent transfers received by households, value\GBP113
364United KingdomUnit labor cost of the total economy\index 2000113
365United KingdomUnit labor cost of the manufacturing sector\index 2001113
366United KingdomUnemployment\persons113
367United KingdomWages, value\GBP113
368United KingdomWage rate of the business sector\GBP113
369United KingdomCompensation rate of government employees\GBP113
370United KingdomWage rate of the manufacturing sector, hourly earnings\index 2001113
371United KingdomCompensation rate of the business sector\yearly salary in GBP113
372United KingdomCompensation of employees, value\GBP113
373United KingdomExports of goods and services, volume, national accounts basis\2001 GBP113
374United KingdomFactor income from abroad, volume, balance of payments basis\GBP113
375United KingdomHousehold disposable income, real\GBP113
376United KingdomProperty income received by households, value113
377United KingdomGovernment current disbursements, value\GBP113
378United KingdomCurrent disbursements of households, value\GBP113
379United KingdomGovernment current receipts, value\GBP113
380United KingdomCurrent receipts of households, value\GBP113
381United KingdomSelf-employment income received by households, value\GBP113
382United KingdomExports prices113
383United KingdomImports prices113
384United KingdomTerms of trade113
385United KingdomOvernight interbank min (percent per annum, AQM, DEC, average)1n12
386United KingdomUnited Kingdom - PPI/WPI (index number, 2000=100, AQM, DEC, average)113
387United KingdomUnited Kingdom - CPI: all items (index number, 2000=100, AQM, DEC, average)113
388United KingdomFTSE 100113
389United KingdomOther investment assets1n12
390United KingdomOther investment liab., N.I.E.1n12
391United KingdomUnited Kingdom\cyclical indicators\surveys of manufacturing industry:\current level113
392United KingdomCyclical indicators\surveys of manufacturing industry:\composite industrial0n10
393United KingdomCyclical indicators\consumer opinion on economic and financial0n10
394United KingdomGovernment saving (net), value\GBP0n10
395United KingdomUnemployment rate\percent0n10
396United KingdomDirect investment abroad0n10
397United KingdomDir. invest. in Rep. Econ.., N.I.E.0n10
398United KingdomPortfolio investment assets0n10
399United KingdomPortfolio investment liab., N.I.E.0n10
400United KingdomFinancial account, N.I.E.0n10
401United StatesBalance of income, value, balance of payments basis\U.S. dollar1n12
402United StatesCurrent account, value in US$\U.S. dollar1n12
403United StatesGovernment consumption of fixed capital, value\U.S. dollar113
404United StatesPrivate final consumption expenditure, volume\U.S. dollar113
405United StatesEmployment, country specific, variable a\U.S. dollar113
406United StatesDependent employment\U.S. dollar113
407United StatesDependent employment of the business sector\U.S. dollar113
408United StatesGovernment employment\U.S. dollar113
409United StatesSelf-employed\U.S. dollar113
410United StatesTotal employment\U.S. dollar113
411United StatesEmployment of the business sector\U.S. dollar113
412United StatesReal Effective exchange rate, 2000 = 100, ULC-based113
413United StatesGross domestic product, volume, market prices\U.S. dollar113
414United StatesPrivate nonresidential fixed capital formation, volume\U.S. dollar113
415United StatesGovernment fixed capital formation, volume\U.S. dollar113
416United StatesIndustrial production\U.S. dollar113
417United StatesPrivate total fixed capital formation, volume\U.S. dollar113
418United StatesLong-term interest rate on government bonds\U.S. dollar1n12
419United StatesLong-term interest rate on corporate bonds\U.S. dollar1n12
420United StatesShort-term interest rate\U.S. dollar1n12
421United StatesGross total fixed capital formation, volume\U.S. dollar113
422United StatesCapital stock of the business sector, volume\U.S. dollar113
423United StatesCapital stock, housing, volume\U.S. dollar113
424United StatesLabor force\U.S. dollar113
425United StatesLabor force participation rate\U.S. dollar1n12
426United StatesImports of goods and services, volume, national accounts basis\U.S. dollar113
427United StatesMoney supply, narrow definition: base money, M1 or M2\U.S. dollar113
428United StatesMoney supply, broad definition: M2 or M3\U.S. dollar113
429United StatesFactor income paid abroad, volume, balance of payments basis\U.S. dollar113
430United StatesLabor productivity of the total economy\U.S. dollar113
431United StatesLabor productivity of the business economy\U.S. dollar113
432United StatesHousehold saving ratio\U.S. dollar1n12
433United StatesCurrent transfers received by households, value\U.S. dollar113
434United StatesUnit labor cost of the total economy\U.S. dollar113
435United StatesUnit labor costs in the business sector\U.S. dollar113
436United StatesUnit labor cost of the manufacturing sector\U.S. dollar113
437United StatesVelocity of money\U.S. dollar113
438United StatesWages, value\U.S. dollar113
439United StatesWages of the government sector, value\U.S. dollar113
440United StatesWage rate of the business sector\U.S. dollar113
441United StatesCompensation rate of government employees\U.S. dollar113
442United StatesWage rate of the manufacturing sector, hourly earnings\U.S. dollar113
443United StatesCompensation rate of the business sector\U.S. dollar113
444United StatesCompensation of employees, value\U.S. dollar113
445United StatesExports of goods and services, volume, national accounts basis\U.S. dollar113
446United StatesFactor income from abroad, volume, balance of payments basis\U.S. dollar113
447United StatesHousehold disposable income, real\U.S. dollar113
448United StatesProperty income received by households, value\U.S. dollar113
449United StatesGovernment current disbursements, value\U.S. dollar113
450United StatesCurrent disbursements of households, value\U.S. dollar113
451United StatesGovernment current receipts, value\U.S. dollar113
452United StatesCurrent receipts of households, value\U.S. dollar113
453United StatesSelf-employment income received by households, value\U.S. dollar113
454United StatesDirect investment abroad1n12
455United StatesDir. invest. in rep. econ., N.I.E.1n12
456United StatesPortfolio investment assets1n12
457United StatesPortfolio investment liab., N.I.E.1n12
458United StatesFinancial account, N.I.E.1n12
459United StatesExports prices113
460United StatesImports prices113
461United StatesTerms of trade113
462United StatesPPI/WPI (index number, 2000=100, AQM, DEC, average)113
463United StatesCPI all items city average (index number, 2000=100, AQM, DEC, average)113
464United StatesShare prices: industrial (index number, AQM, DEC, average)113
465United StatesCyclical indicators\business climate: consumers confidence\1985 = 100 SA0n10
466United StatesUSA PMI business confidence0n10
467United StatesFixed investment in nonresidential construction, volume\U.S. dollar011
468United StatesPrivate residential fixed capital formation, volume\U.S. dollar011
469United StatesFixed investment in machinery and equipment, volume\U.S. dollar011
470United StatesIncrease in stocks, volume\U.S. dollar0n10
471United StatesGovernment saving(net), value\U.S. dollar0n10
472United StatesHousehold saving, value\U.S. dollar011
473United StatesUnemployment\U.S. dollar011
474United StatesUnemployment rate\U.S. dollar0n10
475United StatesProduction/rate of capacity utilisat0n10
476United StatesOther investment assets0n10
477United StatesOther investment liab., N.I.E.0n10
478WorldCommodity Food and Beverage Price Index, 1995 = 100, includes Food and113
479WorldCrude Oil (petroleum), simple average of three spot prices; Dated Brent, West Texas113
480WorldCommodity Metals Price Index, 1995 = 100, includes Copper, Aluminum, Iron Ore,113
481WorldCommodity Nonfuel Price Index, 1995 = 100, includes Food and Beverages and113
482WorldCommodity Industrial Inputs Price Index, 1995 = 100, includes Agricultural Raw011
483G7 excl. FranceGross domestic product, volume, index number113
484G7 excl. FranceConsumer Price Index (SA, 2000=100), index number113
485Euro area excl. FranceGross domestic product, volume, euro113
486Euro area excl. FranceGross domestic product deflator, index number113
Nota bene: Integrated of order 0 = 0, 1 = 1, 2 = 2; not integrated of order 1 or 2 = NS; natural log variables = 1; no transformation = n1. 0: no transformation; 1: logarithm; 2: first difference; 3: first difference of logarithm.
Nota bene: Integrated of order 0 = 0, 1 = 1, 2 = 2; not integrated of order 1 or 2 = NS; natural log variables = 1; no transformation = n1. 0: no transformation; 1: logarithm; 2: first difference; 3: first difference of logarithm.
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1Department of Economics, University of Johannesburg.
2European Department, IMF.
*The authors thank Céline Allard, Luc Everaert, Alessandro Leipold, Rodolfo Luzio, Werner Schule, and Edda Zoli for their comments on an earlier version of the paper, and Sandra Eickmeier for her assistance with the main Matlab codes used. The authors are also indebted to participants at the Bundesbank seminar, at the French Minister of Finance seminar and, especially, to a French discussant of the paper, for their valuable insights. Susan Becker did an efficient data management. Errors and omissions are the authors’ sole responsibility. The views expressed in this study are those of the authors and not of the International Monetary Fund or the University of Johannesburg, with which the authors are affiliated.
3VAR(1) provides a dynamic representation which is parsimonious and quite general (for more details, see Gianonne, 2005). The residuals ut were white noise and thus an autoregressive process of order 1 was chosen.
4Uhlig (2003) shows that two shocks are sufficient to explain 90 percent of the variance at all horizons of real U.S. GNP.
5If, for example, two orthogonal shocks are identified, it is incorrect to identify the first shock as the one corresponding to the first eigenvalue and the second orthogonal shock as the one corresponding to the second eigenvalue (see Uhlig, 2003). The two orthogonal shocks identified generate together the total variation which explanation is being maximized. However, there are multiple possible combinations of those orthogonal shocks all of which will still explain the total variation chosen: as an illustration, and measuring angles in degrees, the pairings of orthogonal shocks with rotation angles {0,90} or {10,100} or {80,170} would be equally acceptable. The grid of the angle of rotation can be different, of course. So the number of possibilities is vast. This paper uses a grid of 30 degrees.
6See Peersman (2005), for more technical details.
7Notice that inequalities include zero responses, some of which are usually excluded in the VAR literature. As shown by Peersman (2005), this may sometimes be unduly restrictive. Peersman shows, for example, that oil prices do react within one quarter to demand and monetary policy shocks. In contrast, imposing the standard contemporaneous zero restriction on oil prices make them appear as exogenous rather than as endogenous responses of an asset price to demand disturbances and monetary policy shocks.
8Clearly, a set of restrictions based on neoclassical model features would produce different results.
9We are deeply grateful to Sandra Eickmeier for having provided us with the main code for the estimation and for her technical support and insights.
10Before one can draw the conclusion that monetary policy contributes little to business cycle fluctuations, it would be advisable to work with a more elaborate sign restriction for monetary policy. This is clearly beyond the scope of this paper.
11The identification of the U.S. shocks required 524 draws, while 639 and 502 draws were necessary for the identification of the G7 and the euro area economic activity shocks, respectively.
12Technically, the variance shares of the common components are independent of the shocks identified.
13From a purely technical viewpoint, it is not correct to weigh the forecast error variance of a given variable by the variance share of its common components; the variance share of the common components is calculated for the first difference of the variable, whereas the forecast error variance refers to the levels of the variable (and specific forecast horizons). Similarly, the stochastic nature of the results should be kept in mind when relating the variance share of the common components to accounting identities based on data that comprises both the common and the idiosyncratic components.
14The impulse-response functions of short- and long-term interest rates are particularly sensitive to the procedure applied to make the series stationary; this is a problem likely related to the difficulty encountered by unit root tests in providing conclusive evidence on the order of integration of those same variables. Results displayed in the paper use differenced interest rate series. The short-term interest rate behavior is difficult toexplain as it falls only marginally following the shock and during a very short period of time.
15Crude oil prices are a simple average of dated Brent, West Texas Intermediate and Dubai Fateh oil prices.
16Anticipating results, French GDP is led exclusively by U.S. GDP in periodicities between two and four years.
17These results are consistent with IMF (2001) and other studies (e.g., Anderton, di Mauro and Moneta, 2004), which stress the role of financial variables and confidence channels in the transmission of macroeconomic disturbances across countries. While in the words of Keynes, “The state of confidence…is a matter to which practical men always pay the closest and most anxious attention,” economist have mostly avoided the issue. The profession has accepted that mood swings are difficult to explain. This paper uses generally accepted measures of confidence as “channels” through which views of the world unfold and affect, for instance, business investment decisions by mechanisms not yet fully identified.
18This outcome is consistent with Eickmeier’s (2004) results on the effects of the U.S. supply shock on German GDP; she finds a positive effect, which is nevertheless not statistically significant. The sign of output shocks transmission is controversial in the empirical literature: those who stress traditional trade channels of transmission posit that a supply shock, by boosting trading partners exports, is transmitted positively (e.g., Kose, Prasad, and Terrones, 2003). In contrast, those who stress inter-industrial specialization and FDI flows hypothesize a negative transmission (e.g., Imbs, 2004).
19The variance share of these variables common components is low. Eickmeier (2004) reports similar results (for Germany).
20The presence of asymmetries in business cycle behavior across countries is well known (e.g., Nadal De Simone, 2007, forthcoming).
21On the one hand, it is not immediately clear why the response of the French economy to U.S. supply and demand shocks differ. A possible reason may be the relatively more important role played by the real sector in the transmission of demand shocks, and the shorter duration of the required changes in the production structure than ensues. Those short-term adjustments to production can be undertaken without changes in capacity and long-term employment. On the other hand, in the literature on optimum currency areas, price and wage flexibility was one key mechanism by which the costs of losing the monetary policy tool by joining a currency area could be diminished. The shock often assumed in that strand of literature was a supply-side shock, i.e., a change in preferences or technology. On this vein, this paper results seem to suggest that the French economy has less price flexibility than the German economy. This is, however, an issue for further research.
22It also increases in the German case: it rises to about 96 percent from just 7 percent in the full sample. This is most likely the result of the significant output effects of German unification, which may have blurred the underlying forces of economic integration of the German economy into the world.
23These results are consistent with IMF (2001) that reports a growing importance of financial variables in the transmission of shocks across countries over time.
24Compared to wages behavior in the full sample, French wages variance following U.S. shocks increased somewhat.

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