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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. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  2. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
  3. 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.
  4. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Thomas J. Sargent, 2005. "A,B,C's (and D's)'s for Understanding VARS," Levine's Bibliography 172782000000000096, UCLA Department of Economics.
  5. 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.
  6. Luca Gambetti, 2010. "Fiscal Policy, Foresight and the Trade Balance in the U.S," Working Papers 505, Barcelona Graduate School of Economics.
  7. 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.
  8. 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".
  9. 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.
  10. 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.
  11. Marco Lippi & Lucrezia Reichlin, 1994. "VAR analysis, non-fundamental representations, Blashke matrices," ULB Institutional Repository 2013/10151, ULB -- Universite Libre de Bruxelles.
  12. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  13. Giannone, Domenico & Reichlin, Lucrezia, 2006. "Does information help recovering structural shocks from past observations?," Working Paper Series 0632, European Central Bank.
  14. 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.
  15. 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.
  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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