France: Selected Issues

International Monetary Fund

doi:10.5089/9781451813692.002.A001

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France: Selected Issues

Author: International Monetary Fund

Publication Date:: 31 Oct 2006

eISBN:: 9781451813692

Language:: English

Keywords:: ISCR; CR; employer; consumption; firm; lower bound; real GDP; demand shocks' effect; FDI flow; upper bound; Foreign direct investment; Short term interest rates; Real effective exchange rates; Terms of trade

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U.S. shocks explain a large part of French output common components. This paper analyzes the economic implications of two alternative welfare financing reforms: a reduction in payroll taxes funded by an increase in consumption taxes, and the other funded by a new levy on business value added. The importance of financial market constraints and whether the recent mortgage market reform is likely to ease these constraints is assessed. Rechargeable mortgages are attractive and encourage collateralization, but bolder measures are needed to limit legal and other fees.

Abstract

I. France in the Global Economy¹

Objective: This study identifies the main shocks that cause fluctuations in economic activity in France and the channels through which France interacts with the global economy. For that purpose, it uses a large-dimensional structural approximate dynamic factor model.

Results: The paper contains three main findings. First, U.S. shocks, especially demand shocks, play an important role in explaining French economic activity as reflected in the share of the forecast error variance of the 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 the consumer confidence channels seem relatively more relevant for the transmission of supply shocks, while interest rates seem relatively more important for the transmission of demand shocks. Second, indicating France’s increasing regional and global economic integration, the share of its GDP fluctuations explained by the common components has increased over time. G7 (excluding France) economic activity affects French output relatively more via demand shocks, while euro area (excluding France) activity affects it relatively more via supply shocks. Third, there is some tentative evidence of regional components, independent of the global common components, in explaining fluctuations in French economic activity. Finally, country-specific components also contribute.

Policy implications: Given the predominance of exogenous factors affecting French economic activity, the asymmetry in the transmission of shocks across countries—illustrated here by comparing French and German variables responses to U.S. shocks—and the fact that France is part of a currency area, French goods, services, and labor markets should be made as flexible as possible. By facilitating the adjustment of the economy to shocks, income volatility should fall and welfare increase.

A. Introduction

1. 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 their transmission channels sometimes defy standard models. For example, French and German consumer confidence indices and French and U.S. business confidence indices exhibit significant comovement as do the national index of stock prices and the performance of the U.S. economy. Moreover, the role of foreign direct investment (FDI) seems sometimes downplayed in empirical work as a relevant additional link between French and U.S. activity.

2. New statistical techniques allow a more reliable extrication of global factors and identification of the channels via which they interact with the French economy. 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 Fernandez-Villaverde, Rubio-Ramirez, and Sargent, 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) and a look inside the “black box” of factor models (Forni, Giannone, Lippi, and Reichlin, 2005). 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.

3. This paper continues and expands the staff’s empirical work on 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.

4. This study contains three main findings. First, U.S. shocks, especially demand shocks, 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. G7 (excluding France) economic activity affects 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.

5. This paper is organized as follows: Section B discusses the model and the economic conditions for the identification of structural shocks. Section C explains the data, data transformation procedures, and the estimation technique. Section D discusses the econometric results on the source of the shocks and the channels of transmission. The last section discusses the policy implications of the paper.

B. Methodology

6. The methodology used in this paper comprises two main steps: First, an estimation of the common components of a large panel of data, and second, the identification of a limited 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);
Decomposition of each time series into two unobserved parts: a common component, driven by shocks common to all series, and an idiosyncratic component;
Writing of the series’ common components as a VAR of low order (often of order one) to represent the reduced form of the model;
Estimation of the VAR to obtain the coefficients matrix and the reduced-form residuals.
Orthogonalization of these residuals to obtain the impulse-response functions and forecast error variances;
Assuming 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;
Computation of the impulse-response functions and the variance decomposition of the first few principal component shocks (e.g., the first two, neglect others);
Recovery of 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; and
Construction of 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.

The Model

7. 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 weak 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).

8. The intuition behind the approximate dynamic factor model analysis is simple. A vector of time series Y_t = (y_1t, y_2t,…, y_Nt)′ can be represented as the sum of two latent components, a common component X_t =(x_1t, x_2t,…, x_Nt)′ and an idiosyncratic component $Ξ_{t} = (ε_{1 t}, ε_{2 t,} ..., ε_{N t})^{'}$

\begin{matrix} \begin{array}{l} Y_{t} = X_{t} + Ξ_{t} \\ Y_{t} = C F_{t} + Ξ_{t} \end{array} & (1) \end{matrix}

where F_t=(f_1t, f_2t,…, f_rt)′ is a vector of r common factors and $C = {(c_{1}^{'}, c_{2}^{'}, ..., c_{N}^{'})}^{'}$ is a N ×r matrix of factor loadings, with r << N. The common component X_t, which is a linear combination of common factors, is driven by a limited number of common shocks, which are the same for all variables. Nevertheless, the effects of the 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 F_t as additional static factors. Thus, common factors include both lagged and contemporaneous factors.

9. Identification of the common components requires the number of series to 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(Y_t).

\begin{matrix} X_{t} = V V' Y_{t}, & (2) \end{matrix}

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

\begin{matrix} F_{t} = V' Y_{t} . & (3) \end{matrix}

From (1), the idiosyncratic component is

\begin{matrix} Ξ_{t} = Y_{t} - X_{t} . & (4) \end{matrix}

From all the more or less formal criteria to determine the number of static factors r, Bai and Ng (2002) information criteria was selected. As in Forni and others (2005), F_t was approximated by an autoregressive representation of order 1²:

\begin{matrix} F_{t} = B F_{t - 1} + u_{t}, & (5) \end{matrix}

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

Economic conditions for identification

10. Once the process followed by the common components is postulated, 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, 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.³ This implies maximizing the explanation of the chosen variance of the k-step ahead forecast error of GDP with a reduced number of shocks.⁴ To this end, k-step ahead prediction errors u_t are decomposed into k mutually orthogonal innovations using the Cholesky decomposition. The lower triangular Cholesky matrix A is such that u_t = Av_t and $E (v_{t} v_{t}^{'}) = I .$ Hence,

\begin{matrix} cov (u_{t}) = A E (v_{t} v^{'}_{t}) A^{'} = A A^{'} . & (6) \end{matrix}

11. Next, impulse-response functions are calculated. Following the example in which the variable of interest is U.S. GDP, the impulse-response function of y_it in period k to the identified shock is obtained as follows,

\begin{matrix} R_{i k} = c_{i} B^{k} A & (7) \end{matrix}

with c_i the ith row of factor loadings of C and with a corresponding variance-covariance matrix $\sum_{j = 0}^{k} R_{i j} R_{i j}^{'}$ .

Second, suppose that an identified shock is linearly correlated to the fundamental forces ω_t= (ω_1t,ω_2t,…,ω_rt)′ behind U.S. GDP, through the r × r matrix Q. Thus,

\begin{matrix} v_{t} = Q ω_{t} . & (8) \end{matrix}

12. The identification procedure involves maximizing the forecast error variance of the variable of interest. 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

\begin{matrix} σ^{2} (k) = \sum_{j = 0}^{k} (R_{i j} q_{l}) (R_{i j} q_{l})' & (9) \end{matrix}

where i is, in our example, U.S. GDP, and q₁ is the first column of Q. The column q₁ is selected in such a way that $q_{1}^{'} σ^{2} q_{1}$ is maximized, that is

\begin{array}{l} σ^{2} (k) = \sum_{j = 0}^{k} (R_{i j} q_{l}) (R_{i j} q_{l})' \\ = q_{l}^{'} S_{i k} q_{l} \end{array}

where $S_{i k} = \sum_{j = 0}^{k} (k + l - j) R_{i j}^{'} R_{i j} .$

The maximization problem subject to the side constraint $q_{1}^{'} q_{1} = 1,$ can be written as the Lagrangean,

\begin{matrix} L = q_{l}^{'} S_{i k} q_{l} - λ (q_{l}^{'} q_{l} - 1) & (10) \end{matrix}

where λ is the Lagrangean multiplier. From (10), q₁ is the first eigenvector of S_ik 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, …, q_r), where q_l (l=1,…, r) is the eigenvector corresponding to the l^th 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.

13. Orthogonal shocks are finally identified by rotation. If two shocks are identified, following Canova and de Nicoló (2003), the orthogonal shocks vector ω_t = (ω_1t,ω_2t)’ is multiplied by a 2 × 2 orthogonal rotation matrix P of the form:

P = (_{sin (θ) cos (θ)}^{cos (θ) - sin (θ)}),

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 6,191,736,421 × 10¹⁰ (12¹⁰) rotations. Hence, the rotated factor w_t =Pw_t explains in total all the variation measured by the first two eigenvalues. This way, the two principal components ω_i, are associated with the two structural shocks ω_i through the matrix P, and the impulse-response functions of the two structural shocks on all the fundamental forces can be estimated.

14. 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.⁵ 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.⁶

Identification Inequalities

C. Data and Estimation

Data

15. This paper uses a large data panel. It 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 is included, containing such items as 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.

16. For comparison purposes, a shorter time period is also estimated. The data panel for the shorter time period includes the same macroeconomic time series plus a G7 (excluding France) and a euro area (excluding France) real GDP series and two corresponding price series (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.

17. 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, the 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.

Estimation

18. 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.⁷ As discussed in Section B, 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.

19. 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 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.

20. 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, which 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.

21. Sign restrictions on impulse response functions were used to give economic meaning to the structural shocks. Following Peersman (2005), and as in GEM (2004) and other 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. 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.⁹ The same results were obtained when identifying G7 and euro area shocks.¹⁰ The impulse-response functions were 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 were estimated.

D. Econometric Results

U.S. shocks

22. 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 decomposition 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.¹¹ 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.

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/

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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 Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shocks	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.54	0.87	0.30	0.92	0.11	0.05	0.67	0.53
Private investment	0.62	0.71	0.22	0.85	0.19	0.05	0.58	0.56
Personal consumption expenditure	0.32	0.87	0.40	0.93	0.04	0.02	0.33	0.29
Employment	0.60	0.75	0.11	0.82	0.21	0.12	0.83	0.58
Productivity	0.14	0.67	0.21	0.94	0.06	0.01	0.39	0.11
Capacity utilization	0.48	0.12	0.01	0.37	0.61	0.28	0.91	0.35
Government current disbursements	0.58	0.03	0.01	0.57	0.02	0.00	0.21	0.03
Government current receipts	0.25	0.34	0.00	0.37	0.39	0.15	0.77	0.18
Consumer confidence	0.66	0.11	0.01	0.32	0.50	0.32	0.91	0.41
Business confidence	0.74	0.74	0.15	0.86	0.24	0.09	0.79	0.73
Consumer prices	0.71	0.24	0.04	0.64	0.46	0.00	0.48	0.50
Short-term interest rates	0.36	0.15	0.01	0.48	0.83	0.22	0.90	0.36
Long-term interest rates	0.37	0.02	0.00	0.18	0.95	0.16	0.85	0.36
M1	0.44	0.19	0.02	0.38	0.60	0.11	0.81	0.35
Stock prices	0.09	0.56	0.04	0.75	0.02	0.00	0.25	0.05
Wages	0.32	0.31	0.00	0.28	0.42	0.27	0.88	0.23
Exports total	0.38	0.58	0.01	0.65	0.28	0.14	0.88	0.33
Imports total	0.45	0.71	0.22	0.85	0.24	0.06	0.66	0.43
Terms of trade	0.13	0.04	0.01	0.47	0.01	0.01	0.50	0.01
Real effective exchange	0.45	0.39	0.00	0.53	0.54	0.00	0.40	0.42
Current account balance	0.31	0.05	0.00	0.46	0.03	0.01	0.37	0.03
FDI out	0.03	0.04	0.01	0.56	0.26	0.02	0.57	0.01
FDI in	0.00	0.42	0.01	0.50	0.35	0.19	0.86	0.00

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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 Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shocks	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.54	0.87	0.30	0.92	0.11	0.05	0.67	0.53
Private investment	0.62	0.71	0.22	0.85	0.19	0.05	0.58	0.56
Personal consumption expenditure	0.32	0.87	0.40	0.93	0.04	0.02	0.33	0.29
Employment	0.60	0.75	0.11	0.82	0.21	0.12	0.83	0.58
Productivity	0.14	0.67	0.21	0.94	0.06	0.01	0.39	0.11
Capacity utilization	0.48	0.12	0.01	0.37	0.61	0.28	0.91	0.35
Government current disbursements	0.58	0.03	0.01	0.57	0.02	0.00	0.21	0.03
Government current receipts	0.25	0.34	0.00	0.37	0.39	0.15	0.77	0.18
Consumer confidence	0.66	0.11	0.01	0.32	0.50	0.32	0.91	0.41
Business confidence	0.74	0.74	0.15	0.86	0.24	0.09	0.79	0.73
Consumer prices	0.71	0.24	0.04	0.64	0.46	0.00	0.48	0.50
Short-term interest rates	0.36	0.15	0.01	0.48	0.83	0.22	0.90	0.36
Long-term interest rates	0.37	0.02	0.00	0.18	0.95	0.16	0.85	0.36
M1	0.44	0.19	0.02	0.38	0.60	0.11	0.81	0.35
Stock prices	0.09	0.56	0.04	0.75	0.02	0.00	0.25	0.05
Wages	0.32	0.31	0.00	0.28	0.42	0.27	0.88	0.23
Exports total	0.38	0.58	0.01	0.65	0.28	0.14	0.88	0.33
Imports total	0.45	0.71	0.22	0.85	0.24	0.06	0.66	0.43
Terms of trade	0.13	0.04	0.01	0.47	0.01	0.01	0.50	0.01
Real effective exchange	0.45	0.39	0.00	0.53	0.54	0.00	0.40	0.42
Current account balance	0.31	0.05	0.00	0.46	0.03	0.01	0.37	0.03
FDI out	0.03	0.04	0.01	0.56	0.26	0.02	0.57	0.01
FDI in	0.00	0.42	0.01	0.50	0.35	0.19	0.86	0.00

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Table 1b.

Forecast Error Variance of the Common Components of USA Variables Explained by the USA Supply Shock and the Demand Shock, 1980-2003 ^1/

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Table 1b.

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 Shock	Confidence Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.43	0.23	0.01	0.30	0.34	0.22	0.85	0.24
Private investment	0.67	0.28	0.01	0.35	0.11	0.08	0.74	0.26
Personal consumption expenditure	0.20	0.40	0.00	0.36	0.02	0.04	0.66	0.08
Employment	0.65	0.06	0.01	0.51	0.20	0.05	0.66	0.17
Productivity	0.22	0.60	0.00	0.47	0.11	0.09	0.73	0.16
Capacity utilization	0.57	0.53	0.07	0.72	0.01	0.01	0.32	0.31
Government current disbursements	0.88	0.09	0.00	0.43	0.06	0.00	0.20	0.14
Government current receipts	0.73	0.00	0.00	0.46	0.10	0.00	0.29	0.08
Consumer confidence	0.47	0.51	0.12	0.89	0.24	0.01	0.61	0.36
Business confidence	0.73	0.02	0.01	0.56	0.16	0.06	0.68	0.13
Consumer prices	0.84	0.07	0.00	0.45	0.15	0.00	0.22	0.18
Short-term interest rates	0.20	0.12	0.02	0.54	0.76	0.21	0.88	0.18
Long-term interest rates	0.31	0.12	0.02	0.47	0.84	0.19	0.88	0.29
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.05	0.57	0.09	0.76	0.04	0.00	0.40	0.03
Wages	0.75	0.14	0.04	0.71	0.19	0.00	0.41	0.25
Exports total	0.42	0.03	0.01	0.19	0.89	0.48	0.95	0.39
Imports total	0.37	0.12	0.01	0.28	0.46	0.24	0.86	0.21
Terms of trade	0.42	0.29	0.02	0.60	0.69	0.03	0.66	0.41
Real effective exchange	0.18	0.13	0.00	0.33	0.72	0.01	0.69	0.15
Current account balance	0.03	0.64	0.27	0.86	0.26	0.01	0.53	0.02
FDI out	0.00	0.62	0.03	0.70	0.32	0.21	0.93	0.00
FDI in	0.01	0.15	0.01	0.51	0.75	0.08	0.75	0.00

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

Table 1b.

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 Shock	Confidence Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.43	0.23	0.01	0.30	0.34	0.22	0.85	0.24
Private investment	0.67	0.28	0.01	0.35	0.11	0.08	0.74	0.26
Personal consumption expenditure	0.20	0.40	0.00	0.36	0.02	0.04	0.66	0.08
Employment	0.65	0.06	0.01	0.51	0.20	0.05	0.66	0.17
Productivity	0.22	0.60	0.00	0.47	0.11	0.09	0.73	0.16
Capacity utilization	0.57	0.53	0.07	0.72	0.01	0.01	0.32	0.31
Government current disbursements	0.88	0.09	0.00	0.43	0.06	0.00	0.20	0.14
Government current receipts	0.73	0.00	0.00	0.46	0.10	0.00	0.29	0.08
Consumer confidence	0.47	0.51	0.12	0.89	0.24	0.01	0.61	0.36
Business confidence	0.73	0.02	0.01	0.56	0.16	0.06	0.68	0.13
Consumer prices	0.84	0.07	0.00	0.45	0.15	0.00	0.22	0.18
Short-term interest rates	0.20	0.12	0.02	0.54	0.76	0.21	0.88	0.18
Long-term interest rates	0.31	0.12	0.02	0.47	0.84	0.19	0.88	0.29
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.05	0.57	0.09	0.76	0.04	0.00	0.40	0.03
Wages	0.75	0.14	0.04	0.71	0.19	0.00	0.41	0.25
Exports total	0.42	0.03	0.01	0.19	0.89	0.48	0.95	0.39
Imports total	0.37	0.12	0.01	0.28	0.46	0.24	0.86	0.21
Terms of trade	0.42	0.29	0.02	0.60	0.69	0.03	0.66	0.41
Real effective exchange	0.18	0.13	0.00	0.33	0.72	0.01	0.69	0.15
Current account balance	0.03	0.64	0.27	0.86	0.26	0.01	0.53	0.02
FDI out	0.00	0.62	0.03	0.70	0.32	0.21	0.93	0.00
FDI in	0.01	0.15	0.01	0.51	0.75	0.08	0.75	0.00

^{^1/}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/

^{^1/}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 Shock	Confidence Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.78	0.003	0.001	0.321	0.066	0.001	0.478	0.05
Private investment	0.57	0.039	0.002	0.422	0.110	0.001	0.598	0.08
Personal consumption expenditure	0.78	0.024	0.002	0.341	0.007	0.004	0.273	0.02
Employment	0.87	0.131	0.003	0.444	0.043	0.004	0.302	0.15
Productivity	0.16	0.769	0.051	0.757	0.025	0.006	0.539	0.13
Capacity utilization	0.64	0.144	0.011	0.569	0.048	0.007	0.474	0.12
Government current disbursements	0.83	0.193	0.004	0.524	0.009	0.019	0.392	0.17
Government current receipts	0.76	0.082	0.003	0.371	0.030	0.005	0.283	0.08
Consumer confidence	0.52	0.130	0.005	0.486	0.012	0.007	0.536	0.07
Business confidence	0.62	0.057	0.005	0.440	0.146	0.035	0.636	0.13
Consumer prices	0.56	0.361	0.003	0.498	0.201	0.001	0.224	0.31
Short-term interest rates	0.43	0.158	0.027	0.592	0.601	0.165	0.836	0.33
Long-term interest rates	0.34	0.030	0.010	0.317	0.890	0.364	0.926	0.31
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.09	0.515	0.032	0.619	0.206	0.034	0.645	0.07
Wages	0.87	0.123	0.003	0.537	0.016	0.008	0.286	0.12
Exports total	0.34	0.164	0.007	0.221	0.487	0.283	0.910	0.22
Imports total	0.28	0.066	0.005	0.330	0.499	0.145	0.867	0.16
Terms of trade	0.57	0.287	0.009	0.561	0.670	0.019	0.663	0.55
Real effective exchange	0.31	0.342	0.006	0.569	0.613	0.008	0.585	0.29
Current account balance	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
FDI out	0.01	0.594	0.099	0.815	0.256	0.005	0.388	0.01
FDI in	0.19	0.315	0.045	0.516	0.409	0.040	0.698	0.14

^{^1/}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 Shock	Confidence Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.78	0.003	0.001	0.321	0.066	0.001	0.478	0.05
Private investment	0.57	0.039	0.002	0.422	0.110	0.001	0.598	0.08
Personal consumption expenditure	0.78	0.024	0.002	0.341	0.007	0.004	0.273	0.02
Employment	0.87	0.131	0.003	0.444	0.043	0.004	0.302	0.15
Productivity	0.16	0.769	0.051	0.757	0.025	0.006	0.539	0.13
Capacity utilization	0.64	0.144	0.011	0.569	0.048	0.007	0.474	0.12
Government current disbursements	0.83	0.193	0.004	0.524	0.009	0.019	0.392	0.17
Government current receipts	0.76	0.082	0.003	0.371	0.030	0.005	0.283	0.08
Consumer confidence	0.52	0.130	0.005	0.486	0.012	0.007	0.536	0.07
Business confidence	0.62	0.057	0.005	0.440	0.146	0.035	0.636	0.13
Consumer prices	0.56	0.361	0.003	0.498	0.201	0.001	0.224	0.31
Short-term interest rates	0.43	0.158	0.027	0.592	0.601	0.165	0.836	0.33
Long-term interest rates	0.34	0.030	0.010	0.317	0.890	0.364	0.926	0.31
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.09	0.515	0.032	0.619	0.206	0.034	0.645	0.07
Wages	0.87	0.123	0.003	0.537	0.016	0.008	0.286	0.12
Exports total	0.34	0.164	0.007	0.221	0.487	0.283	0.910	0.22
Imports total	0.28	0.066	0.005	0.330	0.499	0.145	0.867	0.16
Terms of trade	0.57	0.287	0.009	0.561	0.670	0.019	0.663	0.55
Real effective exchange	0.31	0.342	0.006	0.569	0.613	0.008	0.585	0.29
Current account balance	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
FDI out	0.01	0.594	0.099	0.815	0.256	0.005	0.388	0.01
FDI in	0.19	0.315	0.045	0.516	0.409	0.040	0.698	0.14

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

23. 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. Given that the variance share of U.S. GDP common components is 54 percent, the supply shock explains about 47 percent (i.e., 54 percent × 87 percent) and the demand shock 6 percent (i.e., 54 percent × 11 percent) of the error variance of U.S. GDP, respectively.

24. 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).¹² 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.

25. Evidence supports the U.S. origin of the shocks. First, it is noteworthy to stress 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, as in Eickmeier (2005), a dataset containing only U.S. variables. The resulting impulse-response functions were similar to those of the full sample (not shown). In addition, given 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.¹³ Finally, further 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 two and eight years, the business cycle band, indicating that U.S. GDP common components lead French GDP common components at that frequency band.

Channels of transmission of U.S. shocks to France

26. Broadly speaking, U.S. supply shocks are transmitted to France less forcefully than U.S. demand shocks. The variance share of French variables suggest that foreign trade and relative prices—i.e., terms of trade and/or the real effective exchange rate—and FDI flows matter for the transmission of both U.S. shocks to France. 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 of the respective common components. Stock prices and consumer confidence matter most for the transmission of U.S. supply shocks while interest rates matter most for the transmission of U.S. demand shocks: supply shocks explain over 55 percent of the error variance of the French variables and over 50 percent of consumer confidence; demand shocks explain about 80 percent of the error variance of French interest rates.¹⁴ In addition, while U.S. supply shocks have a lasting effect on U.S. and French stock markets, demand shocks’ effects are temporary and relatively small.

27. U.S. supply shocks may seem to be transmitted negatively on French output. While French output is affected negatively by U.S. supply shocks with a median error variance (over the first five years) of 23 percent, the outcome is in fact statistically insignificant.¹⁵ Stock prices are affected positively and in lasting manner. In addition, notice the negative effect on employment and wages and, consistently, the negative effect on consumer confidence. The current account records a surplus as, over time, exports of goods and services increase more than imports. The terms of trade improve somewhat, and the real effective exchange rate appreciates marginally. There is no lasting significant change in the real effective exchange, however. 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 supply shocks; that the outward FDI flows decrease at the end of the five-year period is difficult to explain.¹⁶ The bottom line is that France seems to adjust to the U.S. supply shock.

28. U.S. demand shocks get transmitted positively to France. Over the sample period 1980–2003, U.S. demand shocks of about 1 percent of GDP (over 20 quarters) have a significant positive impact on France’s real GDP of 0.5 percent. Consumption and investment rise in response. Demand drives up French productivity, with benign effects on the price level. Exports rise more than imports in the first 4–6 quarters producing 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. There is a lasting, albeit small, effect on both consumer and business confidence. 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.

29. U.S. shocks affect different EU member countries asymmetrically.¹⁷ A comparison of the error variance of French and German variables reveals a few noteworthy points. Most importantly, U.S. shocks affect French output more than German output. The weighted effect of the U.S. supply shocks on output is 10 percent in France and less than 1 percent in Germany. The U.S. demand shocks effect on output is 14 percent in France and 5 percent in Germany. In addition, while consumer confidence matters more for France than for Germany, the trade channel, the terms of trade, and the real effective exchange rate are relatively more relevant channels for Germany than for France, presumably due to the larger share of foreign trade in German GDP.¹⁸

Is there evidence of increasing interdependence among countries?

30. France’s interdependence has increased over time. The estimation of the model using the time period 1990:Q1–2003:Q4 shows 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 weighted error variance of French GDP explained by U.S. shocks in the full sample period is about ¼, it increases to well over ½ when the recent sample period is used (Table 3).¹⁹ This increase basically took place through a significant rise in the role of U.S. demand shocks. Besides the enhanced role of the stock market channel in more recent times, the business confidence channel also increased its significance.²⁰ Consistently, the impact of investment in explaining activity fluctuations in France also rose. It also seems that France’s capacity to adjust to U.S. demand shocks became more difficult during the last decade: note, in particular, the relatively lower variance of employment, wages and prices that U.S.-driven demand shocks explain in the recent sample.

31. Adjustment to U.S. shocks varies across countries. When France is compared with Germany, a few points stand out. While the error variance of French variables is lower than that of German variables following U.S. supply shocks, it is very similar following U.S. demand shocks (i.e., compare wages error variances in Table 3a for France and Table 3b for Germany with the respective error variances of GDP).²¹ Consistently, employment does relatively more of the adjustment to U.S. supply shocks in France than in Germany. French prices have lower variance than German prices following U.S. supply shocks, and the real effective exchange rate variance explained by the U.S. shocks is, therefore, much larger in France.

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/

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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 Shock	Confidence Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.64	0.17	0.01	0.45	0.65	0.17	0.89	0.52
Private investment	0.72	0.36	0.01	0.46	0.37	0.15	0.88	0.53
Personal consumption expenditure	0.27	0.16	0.01	0.67	0.38	0.03	0.86	0.15
Employment	0.85	0.48	0.01	0.46	0.21	0.03	0.73	0.59
Productivity	0.42	0.05	0.00	0.47	0.68	0.05	0.82	0.31
Capacity Utilisation	0.73	0.38	0.01	0.75	0.07	0.02	0.47	0.33
Government current disbursements	0.63	0.53	0.01	0.68	0.20	0.06	0.88	0.46
Government current receipts	0.20	0.42	0.01	0.53	0.46	0.17	0.88	0.17
Consumer confidence	0.71	0.37	0.00	0.47	0.10	0.01	0.58	0.33
Business confidence	0.74	0.38	0.01	0.39	0.29	0.04	0.76	0.50
Consumer prices	0.32	0.35	0.00	0.62	0.07	0.01	0.65	0.13
Short-term interest rates	0.46	0.07	0.01	0.46	0.19	0.02	0.56	0.12
Long-term interest rates	0.75	0.03	0.00	0.47	0.22	0.02	0.74	0.19
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.22	0.58	0.01	0.59	0.17	0.01	0.56	0.16
Wages	0.63	0.20	0.01	0.53	0.32	0.02	0.71	0.33
Exports total	0.50	0.16	0.01	0.37	0.47	0.10	0.78	0.31
Imports total	0.50	0.37	0.01	0.46	0.50	0.28	0.90	0.43
Terms of trade	0.33	0.06	0.01	0.49	0.09	0.01	0.39	0.05
Real effective exchange	0.23	0.31	0.01	0.48	0.28	0.01	0.53	0.14
Current account balance	0.12	0.04	0.00	0.64	0.28	0.00	0.41	0.04
FDI out	0.01	0.09	0.01	0.74	0.75	0.08	0.91	0.01
FDI in	0.02	0.07	0.00	0.49	0.06	0.01	0.36	0.00

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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 Shock	Confidence Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.64	0.17	0.01	0.45	0.65	0.17	0.89	0.52
Private investment	0.72	0.36	0.01	0.46	0.37	0.15	0.88	0.53
Personal consumption expenditure	0.27	0.16	0.01	0.67	0.38	0.03	0.86	0.15
Employment	0.85	0.48	0.01	0.46	0.21	0.03	0.73	0.59
Productivity	0.42	0.05	0.00	0.47	0.68	0.05	0.82	0.31
Capacity Utilisation	0.73	0.38	0.01	0.75	0.07	0.02	0.47	0.33
Government current disbursements	0.63	0.53	0.01	0.68	0.20	0.06	0.88	0.46
Government current receipts	0.20	0.42	0.01	0.53	0.46	0.17	0.88	0.17
Consumer confidence	0.71	0.37	0.00	0.47	0.10	0.01	0.58	0.33
Business confidence	0.74	0.38	0.01	0.39	0.29	0.04	0.76	0.50
Consumer prices	0.32	0.35	0.00	0.62	0.07	0.01	0.65	0.13
Short-term interest rates	0.46	0.07	0.01	0.46	0.19	0.02	0.56	0.12
Long-term interest rates	0.75	0.03	0.00	0.47	0.22	0.02	0.74	0.19
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.22	0.58	0.01	0.59	0.17	0.01	0.56	0.16
Wages	0.63	0.20	0.01	0.53	0.32	0.02	0.71	0.33
Exports total	0.50	0.16	0.01	0.37	0.47	0.10	0.78	0.31
Imports total	0.50	0.37	0.01	0.46	0.50	0.28	0.90	0.43
Terms of trade	0.33	0.06	0.01	0.49	0.09	0.01	0.39	0.05
Real effective exchange	0.23	0.31	0.01	0.48	0.28	0.01	0.53	0.14
Current account balance	0.12	0.04	0.00	0.64	0.28	0.00	0.41	0.04
FDI out	0.01	0.09	0.01	0.74	0.75	0.08	0.91	0.01
FDI in	0.02	0.07	0.00	0.49	0.06	0.01	0.36	0.00

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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/

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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 Shock	Confidence Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.42	0.15	0.01	0.60	0.81	0.22	0.97	0.40
Private investment	0.37	0.16	0.01	0.56	0.81	0.22	0.93	0.36
Personal consumption expenditure	0.21	0.16	0.00	0.75	0.60	0.01	0.80	0.16
Employment	0.63	0.59	0.00	0.51	0.16	0.03	0.76	0.47
Productivity	0.42	0.12	0.01	0.61	0.80	0.05	0.83	0.38
Capacity utilization	0.80	0.30	0.00	0.42	0.11	0.01	0.69	0.32
Government current disbursements	0.61	0.52	0.00	0.58	0.00	0.00	0.47	0.32
Government current receipts	0.56	0.27	0.00	0.62	0.29	0.01	0.40	0.31
Consumer confidence	0.64	0.19	0.01	0.59	0.31	0.02	0.69	0.32
Business confidence	0.70	0.17	0.01	0.51	0.57	0.05	0.83	0.52
Consumer prices	0.57	0.37	0.00	0.57	0.01	0.01	0.62	0.22
Short-term interest rates	0.55	0.09	0.01	0.60	0.53	0.03	0.79	0.34
Long-term interest rates	0.37	0.02	0.00	0.47	0.21	0.01	0.74	0.09
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.30	0.56	0.01	0.59	0.25	0.01	0.67	0.24
Wages	0.63	0.29	0.01	0.82	0.33	0.00	0.57	0.39
Exports total	0.39	0.44	0.01	0.51	0.30	0.09	0.83	0.29
Imports total	0.39	0.45	0.01	0.54	0.46	0.22	0.91	0.36
Terms of trade	0.24	0.14	0.01	0.46	0.19	0.02	0.63	0.08
Real effective exchange	0.15	0.47	0.01	0.54	0.21	0.03	0.79	0.10
Current account balance	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
FDI out	0.01	0.22	0.01	0.65	0.06	0.02	0.40	0.00
FDI m	0.23	0.31	0.01	0.41	0.24	0.02	0.63	0.13

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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 Shock	Confidence Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.42	0.15	0.01	0.60	0.81	0.22	0.97	0.40
Private investment	0.37	0.16	0.01	0.56	0.81	0.22	0.93	0.36
Personal consumption expenditure	0.21	0.16	0.00	0.75	0.60	0.01	0.80	0.16
Employment	0.63	0.59	0.00	0.51	0.16	0.03	0.76	0.47
Productivity	0.42	0.12	0.01	0.61	0.80	0.05	0.83	0.38
Capacity utilization	0.80	0.30	0.00	0.42	0.11	0.01	0.69	0.32
Government current disbursements	0.61	0.52	0.00	0.58	0.00	0.00	0.47	0.32
Government current receipts	0.56	0.27	0.00	0.62	0.29	0.01	0.40	0.31
Consumer confidence	0.64	0.19	0.01	0.59	0.31	0.02	0.69	0.32
Business confidence	0.70	0.17	0.01	0.51	0.57	0.05	0.83	0.52
Consumer prices	0.57	0.37	0.00	0.57	0.01	0.01	0.62	0.22
Short-term interest rates	0.55	0.09	0.01	0.60	0.53	0.03	0.79	0.34
Long-term interest rates	0.37	0.02	0.00	0.47	0.21	0.01	0.74	0.09
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.30	0.56	0.01	0.59	0.25	0.01	0.67	0.24
Wages	0.63	0.29	0.01	0.82	0.33	0.00	0.57	0.39
Exports total	0.39	0.44	0.01	0.51	0.30	0.09	0.83	0.29
Imports total	0.39	0.45	0.01	0.54	0.46	0.22	0.91	0.36
Terms of trade	0.24	0.14	0.01	0.46	0.19	0.02	0.63	0.08
Real effective exchange	0.15	0.47	0.01	0.54	0.21	0.03	0.79	0.10
Current account balance	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
FDI out	0.01	0.22	0.01	0.65	0.06	0.02	0.40	0.00
FDI m	0.23	0.31	0.01	0.41	0.24	0.02	0.63	0.13

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

32. Recent history further confirms the predominant role played by U.S. shocks. 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 59 percent (7 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.

33. There is limited evidence of “regional shocks.” When the shock is to the euro area activity measure (excluding France), the error variance of French GDP explained rises from 52 percent to 63 percent (Table 4). In addition, the cross-spectrum of EU and French GDP common components is very similar to the one of U.S. and French GDP common components (Figure 2), with, however, one caveat. EU GDP common components lead France’s common components in the very long run, i.e., in periodicities beyond eight years, where there is no significant comovement between the United States and France. Finally, the cross-spectrum of U.S. and EU GDP common components clearly shows that United States leads the EU (Figure 3). The results suggest that 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 suggesting a relatively minor role to regional factors (e.g., Kose, Otrok, and Whiteman, 2003, and Nadal De Simone, 2003).

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/

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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 Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.64	0.11	0.01	0.35	0.81	0.41	0.96	0.59
Private investment	0.72	0.33	0.01	0.52	0.43	0.17	0.90	0.55
Personal consumption expenditure	0.27	0.18	0.01	0.44	0.31	0.07	0.80	0.13
Employment	0.85	0.47	0.01	0.61	0.28	0.03	0.74	0.63
Productivity	0.42	0.15	0.01	0.41	0.79	0.16	0.91	0.39
Capacity utilization	0.73	0.32	0.03	0.73	0.09	0.01	0.37	0.30
Government current disbursements	0.63	0.59	0.01	0.79	0.16	0.03	0.77	0.47
Government current receipts	0.20	0.34	0.01	0.60	0.55	0.11	0.85	0.17
Consumer confidence	0.71	0.38	0.01	0.54	0.18	0.01	0.60	0.39
Business confidence	0.74	0.32	0.01	0.49	0.46	0.09	0.81	0.57
Consumer prices	0.32	0.52	0.00	0.71	0.00	0.00	0.39	0.17
Short-term interest rates	0.46	0.09	0.01	0.39	0.57	0.07	0.72	0.30
Long-term interest rates	0.75	0.09	0.00	0.39	0.58	0.19	0.89	0.50
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.22	0.58	0.01	0.70	0.15	0.00	0.34	0.16
Wages	0.63	0.16	0.02	0.41	0.52	0.07	0.79	0.43
Exports total	0.50	0.06	0.01	0.32	0.83	0.32	0.90	0.44
Imports total	0.50	0.27	0.01	0.55	0.69	0.35	0.95	0.48
Terms of trade	0.33	0.02	0.00	0.38	0.43	0.01	0.55	0.15
Real effective exchange	0.23	0.20	0.01	0.53	0.48	0.01	0.51	0.16
Current account balance	0.12	0.08	0.00	0.53	0.03	0.00	0.43	0.01
FDI out	0.01	0.07	0.01	0.57	0.56	0.09	0.83	0.01
FDI in	0.02	0.23	0.00	0.43	0.30	0.01	0.58	0.01

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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 Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.64	0.11	0.01	0.35	0.81	0.41	0.96	0.59
Private investment	0.72	0.33	0.01	0.52	0.43	0.17	0.90	0.55
Personal consumption expenditure	0.27	0.18	0.01	0.44	0.31	0.07	0.80	0.13
Employment	0.85	0.47	0.01	0.61	0.28	0.03	0.74	0.63
Productivity	0.42	0.15	0.01	0.41	0.79	0.16	0.91	0.39
Capacity utilization	0.73	0.32	0.03	0.73	0.09	0.01	0.37	0.30
Government current disbursements	0.63	0.59	0.01	0.79	0.16	0.03	0.77	0.47
Government current receipts	0.20	0.34	0.01	0.60	0.55	0.11	0.85	0.17
Consumer confidence	0.71	0.38	0.01	0.54	0.18	0.01	0.60	0.39
Business confidence	0.74	0.32	0.01	0.49	0.46	0.09	0.81	0.57
Consumer prices	0.32	0.52	0.00	0.71	0.00	0.00	0.39	0.17
Short-term interest rates	0.46	0.09	0.01	0.39	0.57	0.07	0.72	0.30
Long-term interest rates	0.75	0.09	0.00	0.39	0.58	0.19	0.89	0.50
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.22	0.58	0.01	0.70	0.15	0.00	0.34	0.16
Wages	0.63	0.16	0.02	0.41	0.52	0.07	0.79	0.43
Exports total	0.50	0.06	0.01	0.32	0.83	0.32	0.90	0.44
Imports total	0.50	0.27	0.01	0.55	0.69	0.35	0.95	0.48
Terms of trade	0.33	0.02	0.00	0.38	0.43	0.01	0.55	0.15
Real effective exchange	0.23	0.20	0.01	0.53	0.48	0.01	0.51	0.16
Current account balance	0.12	0.08	0.00	0.53	0.03	0.00	0.43	0.01
FDI out	0.01	0.07	0.01	0.57	0.56	0.09	0.83	0.01
FDI in	0.02	0.23	0.00	0.43	0.30	0.01	0.58	0.01

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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/

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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 Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.64	0.77	0.09	0.91	0.21	0.05	0.88	0.63
Private investment	0.72	0.80	0.12	0.92	0.04	0.02	0.74	0.61
Personal consumption expenditure	0.27	0.53	0.01	0.78	0.07	0.03	0.82	0.16
Employment	0.85	0.80	0.07	0.88	0.04	0.01	0.62	0.71
Productivity	0.42	0.20	0.00	0.48	0.65	0.12	0.91	0.35
Capacity utilization	0.73	0.26	0.05	0.50	0.15	0.01	0.52	0.30
Government current disbursements	0.63	0.67	0.15	0.93	0.10	0.01	0.52	0.49
Government current receipts	0.20	0.93	0.08	0.91	0.03	0.02	0.74	0.19
Consumer confidence	0.71	0.61	0.04	0.78	0.04	0.01	0.58	0.46
Business confidence	0.74	0.84	0.08	0.88	0.04	0.02	0.72	0.65
Consumer prices	0.32	0.30	0.01	0.75	0.19	0.00	0.39	0.16
Short-term interest rates	0.46	0.32	0.02	0.64	0.32	0.03	0.69	0.29
Long-term interest rates	0.75	0.17	0.01	0.72	0.34	0.01	0.65	0.38
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.22	0.67	0.01	0.70	0.09	0.00	0.36	0.17
Wages	0.63	0.66	0.03	0.76	0.14	0.03	0.80	0.51
Exports total	0.50	0.66	0.05	0.77	0.19	0.04	0.77	0.43
Imports total	0.50	0.93	0.25	0.95	0.06	0.02	0.73	0.49
Terms of trade	0.33	0.24	0.01	0.56	0.14	0.01	0.45	0.13
Real effective exchange	0.23	0.74	0.01	0.71	0.03	0.01	0.56	0.18
Current account balance	0.12	0.11	0.01	0.59	0.00	0.00	0.36	0.01
FDI out	0.01	0.41	0.02	0.65	0.13	0.03	0.62	0.01
FDI in	0.02	0.03	0.01	0.42	0.38	0.01	0.59	0.01

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

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 Intervals		Demand Shock	Confidence Intervals		Total Forecast Error Variance
	Variance Shares of the Common Components	Supply Shock	Lower Bound	Upper Bound	Demand Shock	Lower Bound	Upper Bound	Total Forecast Error Variance
GDP	0.64	0.77	0.09	0.91	0.21	0.05	0.88	0.63
Private investment	0.72	0.80	0.12	0.92	0.04	0.02	0.74	0.61
Personal consumption expenditure	0.27	0.53	0.01	0.78	0.07	0.03	0.82	0.16
Employment	0.85	0.80	0.07	0.88	0.04	0.01	0.62	0.71
Productivity	0.42	0.20	0.00	0.48	0.65	0.12	0.91	0.35
Capacity utilization	0.73	0.26	0.05	0.50	0.15	0.01	0.52	0.30
Government current disbursements	0.63	0.67	0.15	0.93	0.10	0.01	0.52	0.49
Government current receipts	0.20	0.93	0.08	0.91	0.03	0.02	0.74	0.19
Consumer confidence	0.71	0.61	0.04	0.78	0.04	0.01	0.58	0.46
Business confidence	0.74	0.84	0.08	0.88	0.04	0.02	0.72	0.65
Consumer prices	0.32	0.30	0.01	0.75	0.19	0.00	0.39	0.16
Short-term interest rates	0.46	0.32	0.02	0.64	0.32	0.03	0.69	0.29
Long-term interest rates	0.75	0.17	0.01	0.72	0.34	0.01	0.65	0.38
M1	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.
Stock prices	0.22	0.67	0.01	0.70	0.09	0.00	0.36	0.17
Wages	0.63	0.66	0.03	0.76	0.14	0.03	0.80	0.51
Exports total	0.50	0.66	0.05	0.77	0.19	0.04	0.77	0.43
Imports total	0.50	0.93	0.25	0.95	0.06	0.02	0.73	0.49
Terms of trade	0.33	0.24	0.01	0.56	0.14	0.01	0.45	0.13
Real effective exchange	0.23	0.74	0.01	0.71	0.03	0.01	0.56	0.18
Current account balance	0.12	0.11	0.01	0.59	0.00	0.00	0.36	0.01
FDI out	0.01	0.41	0.02	0.65	0.13	0.03	0.62	0.01
FDI in	0.02	0.03	0.01	0.42	0.38	0.01	0.59	0.01

^{^1/}Forecast horizon is 20 quarters and refers to the levels of the series. Confidence intervals are constructed using bootstrapping methods.

34. Asymmetries in business cycle transmission persist during the shorter sample period. 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 most likely the outcome of the relatively richer vertical and horizontal integration between French and regional firms than between French and other non-euro area G7 countries’ firms. 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. Similarly, the large increase in the explained 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.

E. Conclusion and Policy Implications

35. French output behavior is significantly affected by U.S. shocks. This study found that U.S. shocks, especially demand shocks, 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 regional and country-specific 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.

36. France has become more integrated into the global economy. 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. In addition, and compared to Germany, the French economy reacts to foreign shocks relying relatively more on employment and productivity changes than on changes in wages.

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

38. The French economy would benefit from further structural reforms increasing 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 slower. 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 price stability in France will be higher the more rapidly the shock is transmitted into wages. These conclusions are hardly unexpected, but the framework used in this paper has evinced, in a robust way, their policy relevance.

39. The asymmetry in the transmission of U.S. shocks to different euro area members further supports calls to increase markets flexibility. This asymmetry—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

CU	Capacity utilization
GD	Government current disbursements
GR	Government current receipts
GS	Government net savings
C Confidence	Consumer confidence
B Confidence	Business confidence
CPI	Consumer price index
ST Int	Short-term interest rate
LT Int	Long-term interest rate on government bonds
SP	Share price index
TT	Terms of trade
REER	Real effective exchange rate
CA	Current account of the balance of payments
FDI	Foreign direct investment flows

APPENDIX I. Macroeconomic Series

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 = nl. 0: no transformation; 1: logarithm; 2: first difference; 3: first difference of logarithm.

Number	Country	Variable Name	Unit Root	Log	Treatment
1	France	Balance of income, value, balance of payments basis	1	nl	2
2	France	Current account, value	1	nl	2
3	France	Government consumption of fixed capital, value	1	1	3
4	France	Private final consumption expenditure, volume \ euros 1995	1	1	3
5	France	Dependent employment \ persons	1	1	3
6	France	Dependent employment of the business sector \ persons	1	1	3
7	France	Government employment \ persons	1	1	3
8	France	Self-employed \ persons	1	1	3
9	France	Total employment \ persons	1	1	3
10	France	Exchange rate, index of US$ per local currency \ index	1	1	3
11	France	Employment of the business sector \ persons	1	1	3
12	France	Real Effective exchange rate, 2000 = 100, ULC-based	1	1	3
13	France	Gross domestic product, volume, market prices \ euros 1995	1	1	3
14	France	Private nonresidential fixed capital formation, volume \ euros 1995	1	1	3
15	France	Fixed investment in nonresidential construction, volume	1	1	3
16	France	Government fixed capital formation, volume \ euros 1995	1	1	3
17	France	Private residential fixed capital formation, volume \ euros 1995	1	1	3
18	France	Fixed investment in machinery and equipment, volume \ euros	1	1	3
19	France	Industrial production \ index 1995	1	1	3
20	France	Private total fixed capital formation, volume \ euros 1995	1	1	3
21	France	Long-term interest rate on government bonds \ percent	1	nl	2
22	France	Gross total fixed capital formation, volume \ euros 1995	1	1	3
23	France	Labor force \ persons	1	1	3
24	France	Labor force participation rate	1	1	3
25	France	Imports of goods and services, volume, national accounts basis \ euros	1	1	3
26	France	Factor income paid abroad, volume, balance of payments basis \ local currency	1	1	3
27	France	Labor productivity of the total economy \ index 2000	1	1	3
28	France	Labor productivity of the business economy \ euros	1	1	3
29	France	Government saving (net), value \ euros	1	nl	2
30	France	Household saving ratio \ percent	1	nl	2
31	France	Current transfers received by households, value \ euros	1	1	3
32	France	Unit labor cost of the total economy \ index 2000	1	1	3
33	France	Unit labor cost of the manufacturing sector \ index 1995	1	1	3
34	France	Unemployment \ persons	1	1	3
35	France	Unemployment rate \ percent	1	nl	2
36	France	Wages, value \ euros	1	1	3
37	France	Wages of the government sector, value \ euros	1	1	3
38	France	Compensation rate of government employees \ euros	1	1	3
39	France	Wage rate of the manufacturing sector, hourly earnings \ index 1995	1	nl	2
40	France	Compensation rate of the business sector \ yearly salary in euro	1	1	3
41	France	Compensation of employees, value \ euros	1	1	3
42	France	Exports of goods and services, volume, national accounts basis \ euros 1995	1	1	3
43	France	Factor income from abroad, volume, balance of payments basis \ local currency	1	1	3
44	France	Property income received by households, value \ euros	1	1	3
45	France	Government current disbursements, value \ euros	1	1	3
46	France	Current disbursements of households, value \ euros	1	1	3
47	France	Government current receipts, value \ euros	1	1	3
48	France	Current receipts of households, value \ euros	1	1	3
49	France	Self-employment income received by households, value \ euros	1	1	3
50	France	Direct Investment abroad	1	nl	2
51	France	Dir. invest, in rep. econ., N.I.E.	1	nl	2
52	France	Portfolio investment liab., N.I.E.	1	nl	2
53	France	Exports prices	1	1	3
54	France	Imports prices	1	1	3
55	France	Terms of trade	1	1	3
56	France	CPI: 108 cities (index number, 2000=100, AQM, DEC, average)	1	1	3
57	France	France\interest rates\confidence and economic sentiment/share prices SBF 250 / stock	1	1	3
58	France	Treasury bills: 3 months (percent per annum, AQM, DEC, average)	1	nl	2
59	France	Cyclical indicators\surveys of manufacturing industry:\industrial confidence indicator	0	nl	0
60	France	\Cyclical indicators\consumer opinion on economic and financial	0	nl	0
61	France	Fixed investment in construction, volume	0	1	1
62	France	Increase in stocks, volume \ euros 1995	0	nl	0
63	France	Wage rate of the business sector \ euros per	0	1	1
64	France	Household disposable income, real \ euros	0	1	1
65	France	France\cyclical indicators\surveys of manufacturing industry:\current level of capacity	0	1	1
66	France	Portfolio investment assets	0	nl	0
67	France	Other investment assets	0	nl	0
68	France	Other investment liab., N.I.E.	0	nl	0
69	France	Financial account, N.I.E.	0	nl	0
70	Germany	Government consumption of fixed capital, value \ euros	1	1	3
71	Germany	Private final consumption expenditure, volume \ euros 1995	1	1	3
72	Germany	Dependent employment \ persons	1	1	3
73	Germany	Dependent employment of the business sector	1	1	3
74	Germany	Government employment \ persons	1	1	3
75	Germany	Self-employed \ persons	1	1	3
76	Germany	Total employment \ persons	1	1	3
77	Germany	Employment of the business sector	1	1	3
78	Germany	Exchange rate, index of US$ per local currency \ index	1

	Positive Supply Shock	Positive Demand Shocks	Monetary Policy Tightening
GDP	≥0	≥0	≤0
Prices	≤0	≥0	≤0
Interest rate	≤0	≥0	≥0

	Positive Supply Shock	Positive Demand Shocks	Monetary Policy Tightening
GDP	≥0	≥0	≤0
Prices	≤0	≥0	≤0
Interest rate	≤0	≥0	≥0

Topics

Countries

Series

About

Resources

Abstract

I. France in the Global Economy1

A. Introduction

B. Methodology

The Model

Economic conditions for identification

C. Data and Estimation

Data

Estimation

D. Econometric Results

U.S. shocks

Channels of transmission of U.S. shocks to France

Is there evidence of increasing interdependence among countries?

E. Conclusion and Policy Implications

Acronyms

APPENDIX I. Macroeconomic Series

I. France in the Global Economy¹