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Sufficient information in structural VARs

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  • Mario Forni

    ()

  • Luca Gambetti

    ()

Abstract

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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Bibliographic Info

Paper provided by University of Modena and Reggio E., Dept. of Economics 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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Web page: http://www.recent.unimore.it/
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Keywords: Structural VAR; non-fundamentalness; information; FAVAR models; technology shocks;

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References

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  1. 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.
  2. Mario Forni & Domenico Giannone & Marco Lippi & Lucrezia Reichlin, 2008. "Opening the Black Box: Structural Factor Models with Large Cross-Sections," Working Papers ECARES 2008_036, ULB -- Universite Libre de Bruxelles.
  3. Luca Gambetti, 2010. "Fiscal Policy, Foresight and the Trade Balance in the U.S," Working Papers 505, Barcelona Graduate School of Economics.
  4. Giannone, Domenico & Reichlin, Lucrezia, 2006. "Does information help recovering structural shocks from past observations?," Working Paper Series 0632, European Central Bank.
  5. Marco Lippi & Lucrezia Reichlin, 1993. "The dynamic effects of aggregate demand and supply disturbances: comment," ULB Institutional Repository 2013/10159, ULB -- Universite Libre de Bruxelles.
  6. 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.
  7. Alexei Onatski, 2005. "Determining the number of factors from empirical distribution of eigenvalues," Discussion Papers 0405-19, Columbia University, Department of Economics.
  8. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," NBER Working Papers 10220, National Bureau of Economic Research, Inc.
  9. 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.
  10. Marco Lippi & Lucrezia Reichlin, 1994. "VAR analysis, non-fundamental representations, Blashke matrices," ULB Institutional Repository 2013/10151, ULB -- Universite Libre de Bruxelles.
  11. 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.
  12. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What happens after a technology shock?," International Finance Discussion Papers 768, Board of Governors of the Federal Reserve System (U.S.).
  13. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
  14. 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.
  15. 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.
  16. 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.
  17. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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Citations

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Cited by:
  1. Forni, Mario & Gambetti, Luca & Sala, Luca, 2011. "No News in Business Cycles," CEPR Discussion Papers 8274, C.E.P.R. Discussion Papers.
  2. Luca Gambetti, 2012. "Fiscal Foresight, Forecast Revisions and the Effects of Government Spending in the Open Economy," Working Papers 644, Barcelona Graduate School of Economics.
  3. Jarociński, Marek & Maćkowiak, Bartosz, 2013. "Granger-causal-priority and choice of variables in vector autoregressions," Working Paper Series 1600, European Central Bank.

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