Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-09T05:29:12.318Z Has data issue: false hasContentIssue false
  • English
  • Français

VALIDATING DSGE MODELS WITH SVARS AND HIGH-DIMENSIONAL DYNAMIC FACTOR MODELS

Published online by Cambridge University Press:  13 December 2021

Marco Lippi
Marco Lippi*
Affiliation:
Einaudi Institute for Economics and Finance
*
Address correspondence to Marco Lippi, Einaudi Institute for Economics and Finance, Roma, Italy; e-mail: [email protected].
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A popular validation procedure for Dynamic Stochastic General Equilibrium (DSGE) models consists in comparing the structural shocks and impulse-response functions obtained by estimation-calibration of the DSGE with those obtained in an Structural Vector Autoregressions (SVAR) identified by means of some of the DSGE restrictions. I show that this practice can be seriously misleading when the variables used in the SVAR contain measurement errors. If this is the case, for generic values of the parameters of the DSGE, the shocks estimated in the SVAR are not “made of” the corresponding structural shocks plus measurement error. Rather, each of the SVAR shocks is contaminated by noncorresponding structural shocks. We argue that High-Dimensional Dynamic Factor Models are free from this drawback and are the natural model to use in validation procedures for DSGEs.

Type
ARTICLES
Information
Econometric Theory , Volume 39 , Issue 6: SPECIAL ISSUE IN HONOR OF BENEDIKT M PÖTSCHER , December 2023 , pp. 1273 - 1291
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

The author thanks two anonymous referees, the Guest Editor, Manfred Deistler and the Editor, Peter C. B. Phillips, for their very constructive comments, which led to a much improved presentation.

References

REFERENCES

Alessi, L., Barigozzi, M. and Capasso, M. (2011) Nonfundamentalness in structural econometric models: A review. International Statistical Review 79, 16–47.CrossRefGoogle Scholar
Anderson, B. D. and Deistler, M. (2008a) Generalized linear dynamic factor models–A structure theory. In IEEE Conference on Decision and Control. IEEE.CrossRefGoogle Scholar
Anderson, B. D. and Deistler, M. (2008b) Properties of zero-free transfer function matrices. SICE Journal of Control, Measurement and System Integration 1, 284–292.CrossRefGoogle Scholar
Bai, J. and Ng, S. (2002) Determining the number of factors in approximate factor models. Econometrica 70, 191–221.CrossRefGoogle Scholar
Bernanke, B. S. and Boivin, J. (2003) Monetary policy in a data-rich environment. Journal of Monetary Economics 50, 525–546.CrossRefGoogle Scholar
Bernanke, B. S., Boivin, J., and Eliasz, P. S. (2005) Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. The Quarterly Journal of Economics 120, 387–422.Google Scholar
Boivin, J., Giannoni, M. P., and Mihov, I. (2009) Sticky prices and monetary policy: Evidence from disaggregated US data. American Economic Review 99, 350–384.CrossRefGoogle Scholar
Canova, F. (2007) Methods for Applied Macroeconomics . Princeton University Press.CrossRefGoogle Scholar
Chari, V., Kehoe, P. J., and McGrattan, E. R. (2008) Are structural VARs with long-run restrictions useful in developing business cycle theory? Journal of Monetary Economics 55, 1337–1352.CrossRefGoogle Scholar
Christiano, L. J., Eichenbaum, M., and Vigfusson, R. (2007) Assessing structural VARs. In Acemoglu, D., Rogoff, K., and Woodford, M. (eds.) NBER Macroeconomics Annual 2006 , Volume 21, NBER Chapters, pp. 1–106. National Bureau of Economic Research, Inc. Google Scholar
Fernández-Villaverde, J., Rubio-Ramírez, J. F., Sargent, T. J. and Watson, M. W. (2007) ABCs (and Ds) of understanding VARs. American Economic Review 97, 1021–1026.CrossRefGoogle Scholar
Forni, M. and Gambetti, L. (2010) The dynamic effects of monetary policy: A structural factor model approach. Journal of Monetary Economics 57, 203–216.CrossRefGoogle Scholar
Forni, M., Gambetti, L., Lippi, M., and Sala, L. (2020) Common component structural VARs. Working paper no. 15529, Center for Economic Policy Research.Google Scholar
Forni, M., Giannone, D., Lippi, M., and Reichlin, L. (2009) Opening the black box: Structural factor models versus structural VARs. Econometric Theory 25, 1319–1347.CrossRefGoogle Scholar
Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2000) The generalized dynamic factor model: Identification and estimation. The Review of Economics and Statistics 82, 540–554.CrossRefGoogle Scholar
Forni, M. and Lippi, M. (1997) Aggregation and the Microfoundations of Dynamic Macroeconomics . Oxford University Press.CrossRefGoogle Scholar
Forni, M. and Lippi, M. (2001) The generalized dynamic factor model: Representation theory. Econometric Theory 17, 1113–1141.CrossRefGoogle Scholar
Forni, M. and Reichlin, L. (1998) Let’s get real: A factor analytical approach to disaggregated business cycle dynamics. Review of Economic Studies 65, 453–473.CrossRefGoogle Scholar
Giannone, D., Reichlin, L., and Sala, L. (2006) VARs, common factors and the empirical validation of equilibrium business cycle models. Journal of Econometrics 132, 257–279.CrossRefGoogle Scholar
Granger, C. W. J. and Morris, M. J. (1976) Time series modelling and interpretation. Journal of the Royal Statistical Society. Series A 139, 246–257.CrossRefGoogle Scholar
Hallin, M. and Liška, R. (2007) Determining the number of factors in the general dynamic factor model. Journal of the American Statistical Association 102, 603–617.CrossRefGoogle Scholar
Hannan, E. J. and Deistler, M. (1988) The Statistical Theory of Linear Systems . Wiley.Google Scholar
Hansen, L. P. and Sargent, T. J. (1991) Two difficulties in interpreting vector autoregressions. In Hansen, L. P. and Sargent, T. J. (eds.), Rational Expectations Econometrics , pp. 77–120. Westview Press.Google Scholar
Leeb, H. and Pötscher, B. M. (2005) Model selection and inference: Facts and fiction. Econometric Theory 21, 21–59.CrossRefGoogle Scholar
Lippi, M. and Reichlin, L. (1993) The dynamic effects of aggregate demand and supply disturbances: Comment. American Economic Review 83, 644–652.Google Scholar
Lütkepohl, H. (1984) Linear transformations of vector ARMA processes. Journal of Econometrics 26, 283–293.CrossRefGoogle Scholar
Morris, S. D. (2016) VARMA representation of DSGE models. Economics Letters 138, 30–33.CrossRefGoogle Scholar
Rozanov, Y. A. (1967) Stationary Random Processes . Holden Day.Google Scholar
Sargent, T. J. (1989) Two models of measurements and the investment accelerator. Journal of Political Economy 97, 251–287.CrossRefGoogle Scholar
Sims, C. A. and Zha, T. (2006) Does monetary policy generate recessions? Macroeconomic Dynamics 10, 231–272.CrossRefGoogle Scholar
Stock, J. H. and Watson, M. W. (2002a) Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association 97, 1167–1179.CrossRefGoogle Scholar
Stock, J. H. and Watson, M. W. (2002b) Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics 20, 147–162.CrossRefGoogle Scholar
Stock, J. H. and Watson, M. W. (2005) Implications of dynamic factor models for VAR analysis. Working paper no. 11467, NBER.CrossRefGoogle Scholar