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Testing for Sufficient Information in Structural VARs

  • Mario Forni
  • Luca Gambetti

We derive necessary and sufficient conditions under which a set of variables is informationally sufficient, i.e. it contains enough information to estimate the structural shocks with a VAR model. Based on such conditions, we suggest a procedure to test for informational sufficiency. Moreover, we show how to amend the VAR if informational sufficiency is rejected. We apply our procedure to a VAR including TFP, unemployment and per-capita hours worked. We find that the three variables are not informationally sufficient. When adding missing information, the effects of technology shocks change dramatically.

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Paper provided by Barcelona Graduate School of Economics in its series Working Papers with number 536.

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Date of creation: Feb 2011
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Handle: RePEc:bge:wpaper:536
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  1. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  2. Marco Lippi & Lucrezia Reichlin, 1994. "VAR analysis, non-fundamental representations, Blashke matrices," ULB Institutional Repository 2013/10151, ULB -- Universite Libre de Bruxelles.
  3. Luca Gambetti, 2010. "Fiscal Policy, Foresight and the Trade Balance in the U.S," UFAE and IAE Working Papers 852.10, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
  4. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2007. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Staff Report 364, Federal Reserve Bank of Minneapolis.
  5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  6. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Thomas J. Sargent & Mark W. Watson, 2007. "ABCs (and Ds) of Understanding VARs," American Economic Review, American Economic Association, vol. 97(3), pages 1021-1026, June.
  7. Eric M. Leeper & Todd B. Walker & Shu-Chun Susan Yang, 2008. "Fiscal Foresight: Analytics and Econometrics," NBER Working Papers 14028, National Bureau of Economic Research, Inc.
  8. Forni, Mario & Giannone, Domenico & Lippi, Marco & Reichlin, Lucrezia, 2009. "Opening The Black Box: Structural Factor Models With Large Cross Sections," Econometric Theory, Cambridge University Press, vol. 25(05), pages 1319-1347, October.
  9. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
  10. Mario Forni & Luca Gambetti, 2010. "Fiscal Foresight and the Effects of Government Spending," Center for Economic Research (RECent) 049, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
  11. 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.
  12. Lawrence J. Christiano & Martin Eichenbaum & Robert J. Vigfusson, 2003. "What happens after a technology shock?," International Finance Discussion Papers 768, Board of Governors of the Federal Reserve System (U.S.).
  13. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
  14. Domenico Giannone & Lucrezia Reichlin, 2006. "Does information help recovering structural shocks from past observations?," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 455-465, 04-05.
  15. 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.
  16. 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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