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Testing for Sufficient Information in Structural VARs


  • Forni, Mario
  • Gambetti, Luca


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.

Suggested Citation

  • Forni, Mario & Gambetti, Luca, 2011. "Testing for Sufficient Information in Structural VARs," CEPR Discussion Papers 8209, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:8209

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    References listed on IDEAS

    1. 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.
    2. 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.
    3. Lippi, Marco & Reichlin, Lucrezia, 1994. "VAR analysis, nonfundamental representations, blaschke matrices," Journal of Econometrics, Elsevier, vol. 63(1), pages 307-325, July.
    4. 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.
    5. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    6. Mario Forni & Luca Gambetti, 2010. "Fiscal Foresight and the Effects of Government Spending," UFAE and IAE Working Papers 851.10, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    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 254-259, April.
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    14. 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.
    15. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
    16. 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.
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    Cited by:

    1. Mario Forni & Luca Gambetti & Luca Sala, 2014. "No News in Business Cycles," Economic Journal, Royal Economic Society, vol. 124(581), pages 1168-1191, December.
    2. Emily Anderson & Atsushi Inoue & Barbara Rossi, 2016. "Heterogeneous Consumers and Fiscal Policy Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(8), pages 1877-1888, December.
    3. Gubler, Matthias & Hertweck, Matthias S., 2013. "Commodity price shocks and the business cycle: Structural evidence for the U.S," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 324-352.
    4. Karen Davtyan, 2016. "Interrelation among Economic Growth, Income Inequality, and Fiscal Performance: Evidence from Anglo-Saxon Countries," Hacienda Pública Española, IEF, vol. 217(2), pages 37-66, June.
    5. Filippo Ferroni & Benjamin Klaus, 2015. "Euro Area business cycles in turbulent times: convergence or decoupling?," Applied Economics, Taylor & Francis Journals, vol. 47(34-35), pages 3791-3815, July.
    6. repec:eee:eneeco:v:64:y:2017:i:c:p:469-482 is not listed on IDEAS

    More about this item


    FAVAR models; information; non-fundamentalness; Structural VAR; technology shocks.;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy


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