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Sufficient information in structural VARs

  • Mario Forni


  • Luca Gambetti


We derive necessary and sufficient conditions under which a set of variables is information-ally sufficient, i.e. contains enough information to estimate the structural shocks with a VAR model. Based on such conditions, we provide a procedure to test for informational sufficiency. If sufficiency is rejected, we propose a strategy to amend the VAR. Our method can be applied to FAVAR models and can be used to determine how many factors to include in such models. We apply our procedure to a VAR including TFP, unemployment and per-capita hours worked. We find that the three variables are not informationally sucient. When adding missing information, the effects of technology shocks change dramatically.

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Paper provided by University of Modena and Reggio E., Dept. of Economics "Marco Biagi" in its series Center for Economic Research (RECent) with number 062.

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Length: pages 26
Date of creation: Jun 2011
Date of revision:
Handle: RePEc:mod:recent:062
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  1. Eric M. Leeper & Todd B. Walker & Shu‐Chun Susan Yang, 2013. "Fiscal Foresight and Information Flows," Econometrica, Econometric Society, vol. 81(3), pages 1115-1145, 05.
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  8. Forni, Mario & Reichlin, Lucrezia, 1995. "Dynamic Common Factors in Large Cross-Sections," CEPR Discussion Papers 1285, C.E.P.R. Discussion Papers.
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  10. 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.
  11. Forni, Mario & Gambetti, Luca, 2010. "Fiscal Foresight and the Effects of Goverment Spending," CEPR Discussion Papers 7840, C.E.P.R. Discussion Papers.
  12. Beaudry, Paul & Portier, Franck, 2003. "Stock Prices, News and Economic Fluctuations," CEPR Discussion Papers 3844, C.E.P.R. Discussion Papers.
  13. Forni, Mario & Giannone, Domenico & Lippi, Marco & Reichlin, Lucrezia, 2007. "Opening the black box: structural factor models with large cross-sections," Working Paper Series 0712, European Central Bank.
  14. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach," Finance and Economics Discussion Series 2004-03, Board of Governors of the Federal Reserve System (U.S.).
  15. Chari, V.V. & Kehoe, Patrick J. & McGrattan, Ellen R., 2008. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1337-1352, November.
  16. Karel Mertens & MortenO. Ravn, 2010. "Measuring the Impact of Fiscal Policy in the Face of Anticipation: A Structural VAR Approach," Economic Journal, Royal Economic Society, vol. 120(544), pages 393-413, 05.
  17. Eric Leeper & Todd Walker, 2011. "Information Flows and News Driven Business Cycles," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 55-71, January.
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  19. Domenico Giannone & Lucrezia Reichlin, 2006. "Does information help recovering structural shocks from past observations?," ULB Institutional Repository 2013/166169, ULB -- Universite Libre de Bruxelles.
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  22. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
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  28. Gelper, Sarah & Croux, Christophe, 2007. "Multivariate out-of-sample tests for Granger causality," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3319-3329, April.
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  30. Eric M. Leeper & Todd B. Walker & Shu-Chun Susan Yang, 2008. "Fiscal Foresight: Analytics and Econometrics," Caepr Working Papers 2008-013, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
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