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Opening the Black Box: Structural Factor Models versus Structural VARs

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Author Info
Forni, Mario
Lippi, Marco
Reichlin, Lucrezia

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Abstract

In this Paper we study identification in dynamic factor models and argue that factor models are better suited than VARs to provide a structural representation of the macroeconomy. Factor models distinguish measurement errors and other idiosyncratic disturbances from structural macroeconomic shocks. As a consequence, the number of structural shocks is no longer equal to the number of variables included in the information set. In practice, the number of structural shocks turns out to be small, so that only a few restrictions are needed to reach identification. Economic interpretation is then easier. On the other hand, with factor models we can handle much larger information sets - including virtually all existing macroeconomic information. This solves the problems of superior information and fundamentality and enables us to analyse the effects of the shocks on all macroeconomic variables. In the empirical illustration we study a set of 89 US macroeconomic time series, including the series analysed in the seminal paper of King et al. (1991). We find that the system of impulse response functions of these series is non-fundamental and therefore cannot be estimated with a VAR. Moreover, unlike in King et al. (1991), the impulse response functions of the permanent shock are monotonic and therefore more credible if the permanent shock is interpreted as technical change.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 4133.

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Date of creation: Dec 2003
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Handle: RePEc:cpr:ceprdp:4133

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Related research
Keywords: Dynamic factor models; identification; structural VARs;

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Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
E00 - Macroeconomics and Monetary Economics - - General - - - General

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  1. Granger, C. W. J., 1987. "Implications of Aggregation with Common Factors," Econometric Theory, Cambridge University Press, vol. 3(02), pages 208-222, April. [Downloadable!]
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    Other versions:
  3. Forni, Mario & Reichlin, Lucrezia, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Blackwell Publishing, vol. 65(3), pages 453-73, July. [Downloadable!] (restricted)
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  7. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall. [Downloadable!] (restricted)
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  9. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  14. Lippi, Marco & Reichlin, Lucrezia, 1993. "The Dynamic Effects of Aggregate Demand and Supply Disturbances: Comment," American Economic Review, American Economic Association, vol. 83(3), pages 644-52, June. [Downloadable!] (restricted)
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  25. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December. [Downloadable!] (restricted)
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  28. Giannone, Domenico & Reichlin, Lucrezia & Sala, Luca, 2002. "VARs, Common Factors and the Empirical Validation of Equilibrium Business Cycle Models," CEPR Discussion Papers 3701, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  29. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis. [Downloadable!]
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  31. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
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