Andrews, D.W.K., 1993, “Tests for Parameter Instability and Structural Change with Unknown Change Point,” Econometrica, Vol. 61 (4), pp. 821–856.
Andrews, D.W.K. and W. Ploberger, 1993, “Admissibility of the Likelihood Ratio Test when a Nuisance Parameter is Present Only Under the Alternative,” Cowles Foundation Discussion Papers 1058, Cowles Foundation, Yale University.
Bai, J., R.L. Lumsdaine, and J.H. Stock, 1998, “Testing For and Dating Common Breaks in Multivariate Time Series,” The Review of Economic Studies, Vol. 65 (2), pp. 395–432.
Bayoumi, T., and A. Swiston, 2009, “Foreign Entanglements: Estimating the Source and Size of Spillovers Across Industrial Countries,” IMF Staff Papers, Vol. 56 (2), pp. 353–383 (Washington: International Monetary Fund).
Blanchard, O., and D. Quah, 1989, “The Dynamic Effects of Aggregate Demand and Supply Disturbances,” The American Economic Review, Vol. 79 (4), pp. 655–673.
Blanchard, O., and J. Simon, 2001, “The Long and Large Decline in U.S. Output Volatility,” Brookings Papers on Economic Activity 2001 (1), pp. 135–164.
Christiano, L., M. Eichenbaum, and C. Evans, 1999, “Monetary Policy Shocks: What Have We Learned and to What End?” Handbook of Macroeconomics, Vol. 1, Part A, pp. 65–148.
Dees, S., F. Di Mauro, H. Pesaran, and V. Smith, 2007, “Exploring the International Linkages of the Euro Area: A Global VAR Analysis,” Journal of Applied Econometrics, Vol. 22 (1), pp. 1–38.
Doyle, B., and J. Faust, 2005, “Breaks in the Variability and Comovement of G-7 Economic Growth,” The Review of Economics and Statistics, Vol. 87 (4), pp. 721–740.
Galesi, A., and S. Sgherri, 2009, “Regional Financial Spillovers across Europe: A Global VAR Analysis,” IMF Working Paper 09/23 (Washington: International Monetary Fund).
Hansen, B.E., 1997, “Approximate Asymptotic P Values for Structural-Change Test,” Journal of Business & Economic Statistics, Vol. 15 (1), pp. 60–67.
Heathcote, J., and F. Perri, 2004, “Financial Globalization and Real Regionalization,” Journal of Economic Theory, Vol. 119 (1), pp. 207–243.
International Monetary Fund, 2007, World Economic Outlook, October 2007: Globalization and Inequality (Washington: International Monetary Fund).
Juillard, M., P. Karam, D. Laxton, and P. Pesenti, 2006, “Welfare-Based Monetary Policy Rules in and Estimated DSGE Model of the U.S. Economy,” ECB Working Paper No. 613 (Frankfurt: European Central Bank).
Kim, C.J., and C.R. Nelson, 1999, “Has the U.S. Economy Become More Stable? A Bayesian Approach based on a Markov-switching Model of the Business Cycle,” The Review of Economics and Statistics, Vol. 81 (4), pp. 608–616.
Kose, M.A., E.S. Prasad, and M.E. Terrones, 2003, “Financial Integration and Macroeconomic Volatility,” IMF Staff Papers, Vol. 50 (Special Issue), pp. 119–142 (Washington: International Monetary Fund).
Lanne, M., and H. Lütkepohl, 2008, “A Statistical Comparison of Alternative Identification Schemes for Monetary Policy Shocks,” EUI Working Paper ECO 2008/23 (Italy: European University Institute).
Perez, P., D. Osborn, and M. Artis, 2006, “The International Business Cycle in a Changing World: Volatility and the Propagation of Shocks in the G-7,” Open Economies Review, Vol. 17 (3), pp. 255–279.
Pesaran, M., T. Schuermann, and S. Weiner, 2004, “Measuring Regional Interdependencies Using a Global Error-Correction Macroeconomic Model,” Journal of Business & Economic Statistics, Vol. 22 (2), pp. 129–162.
Qu, Z., and P. Perron, 2007, “Estimating and Testing Structural Changes in Multivariate Regressions,” Econometrica, Vol. 75 (2), pp. 459–502.
Rigobon, R., 2003, “Identification through Heteroskedasticity,” The Review of Economics and Statistics, Vol. 85 (4), pp. 777–792.
Stock, J., and M. Watson, 2003, “Has the Business Cycle Changed and Why?” in NBER Macroeconomics Annual (Cambridge, Massachusetts: National Bureau of Economic Research).
Stock, J., and M. Watson, 2005, “Understanding Changes in International Business Cycle Dynamics,” Journal of the European Economic Association, Vol. 3 (5), pp. 405–430.
Uhlig, H., 2005, “What Are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure,” Journal of Monetary Economics, Vol. 52 (2), pp. 381–419.
The authors gratefully acknowledge helpful comments by Sam Ouliaris, David Romer, Kenneth West, Francis Vitek, Martin Evans, Sandra Eickmeier, and participants at the IMF RES and SPR department seminars, the Georgetown University macroeconomics seminar, and the EABCN conference.
The source of the great moderation - smaller underlying shocks or better policies - remains a subject of much debate. See, for example, Kim and Nelson (1999), Blanchard and Simon (2001), Stock and Watson (2003, 2005), Kose, Prasad, and Terrone (2003), Heathcote and Perri (2004), Juillard, Karam, Laxton, and Pesenti (2006), and International Monetary Fund (2007).
Stock and Watson (2005) test for structural breaks in VAR coefficients of G-7 growth data. Doyle and Faust (2005) study the structural change in comovement of shocks using G-7 output, consumption, and investment growth.
A different framework, the Global VAR, also identifies interactions and contemporaneous interrelation among economies but it does not produce orthogonalized errors. See, for example, Pesaran, Schuermann, and Wiener (2004), Dees, Di Mauro, Pesaran, and Smith (2007), and Galesi and Sgherri (2009).
Rigobon (2003) allows the inclusion of unobservable common shocks. In this paper, we assume there are no common shocks in the model. For simplicity, we do not present the corresponding formulas. See the original paper for more details.
The procedure reports the matrix A only if the Gauss GMM estimation converges and returns a matrix A with values of off-diagonal elements less than one.
The group contains 11 small industrial countries given the availability of quarterly data. Description of data and the aggregation method of the ROW economy are in the appendix.
Differing formal statistical tests indicate a range of optimal lag lengths (between 0 and 8 lags). Thus, we choose to follow existing literature in selecting number of lags used in VAR estimation.
We consider two other tests developed by Bai, Lumsdaine, and Stock (1998) and Qu and Perron (2007). The former method allows testing for and dating a common break in multivariate case. The latter test allows for changes in the covariance matrix. However, both methods are limited by either a number of series which have a common break or a number (not more than 10) of parameters that can be changed at once. These limitations make the test results of our model more difficult to interpret and less reliable.
Results of the Hausman test are omitted here for space saving and available upon request.
The contemporaneous correlations are consistently estimated irrespective of the ordering of variable in the reduced-form VAR. Changing the variable ordering amounts to a permutation of the corresponding rows and columns of the average inverse A-matrix reported in the Table 4.
The standard deviations of structural shocks are calculated using the average A-matrix and the reduced-form estimated variance-covariance matrix for the period of 1980:Q1–2007:Q4.
We report results for the 1970s despite the fact that the A-matrix was found to be unstable for the U.K., but blanking out the U.K. spillovers. Even for other regions, the results for the 1970s are only an approximation as the uncertainty about the U.K. A-matrix coefficients matters for other entries. However, give the limited link between the U.K. and other regions, the reported results are probably relatively accurate.
In a similar approach, Dees and Vansteenkiste (2007) treat the financial conditions and oil prices as endogenous variables to identify the trade effects of a U.S. demand shock on the rest of the world using the GVAR framework. They find that the overall effects of a U.S. demand shock are between 1.5 to 5 times larger than of the trade channel only.