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Measuring Nonfundamentalness for Structural VARs

Listed author(s):
  • Stefano Soccorsi

As nonfundamental vector moving averages do not have causal VAR representations, standard structural VAR methods are deemed inappropriate for recovering the economic shocks of general equilibrium models with nonfundamental reduced forms. In the previous literature it has been pointed out that, despite nonfundamentalness, structural VARs may still be good approximating models. I characterize nonfundamentalness as bias depending on the zeros of moving average filters. However, measuring the nonfundamental bias is not trivial because of the simultaneous occurrence of lag truncation bias. I propose a method to disentangle the bias based on population spectral density and derive a measure for the nonfundamental bias in population. In the application, I find that the SVAR exercises of Sims (2012) are accurate because the nonfundamental bias is mild.
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Paper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number ECARES 2016-01.

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Length: 33 p.
Date of creation: Jan 2016
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Handle: RePEc:eca:wpaper:2013/222962
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  1. 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 644-652, June.
  2. 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.
  3. Massimo Franchi & Paolo Paruolo, 2015. "Minimality of State Space Solutions of DSGE Models and Existence Conditions for Their VAR Representation," Computational Economics, Springer;Society for Computational Economics, vol. 46(4), pages 613-626, December.
  4. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
  5. 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.
  6. Mario Forni & Luca Gambetti & Luca Sala, 2016. "VAR Information and the Empirical Validation of DSGE Models," Center for Economic Research (RECent) 119, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
  7. 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, May.
  8. Mario Forni & Luca Gambetti & Luca Sala, 2014. "No News in Business Cycles," Economic Journal, Royal Economic Society, vol. 124(581), pages 1168-1191, December.
  9. Lippi, Marco & Reichlin, Lucrezia, 1994. "VAR analysis, nonfundamental representations, blaschke matrices," Journal of Econometrics, Elsevier, vol. 63(1), pages 307-325, July.
  10. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
  11. Ravenna, Federico, 2007. "Vector autoregressions and reduced form representations of DSGE models," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2048-2064, October.
  12. Barsky, Robert B. & Sims, Eric R., 2011. "News shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 58(3), pages 273-289.
  13. Paul Beaudry & Franck Portier, 2006. "Stock Prices, News, and Economic Fluctuations," American Economic Review, American Economic Association, vol. 96(4), pages 1293-1307, September.
  14. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2014. "Dynamic Factor Models, Cointegration and Error Correction Mechanisms," Working Papers ECARES ECARES 2014-14, ULB -- Universite Libre de Bruxelles.
  15. Lanne, Markku & Saikkonen, Pentti, 2013. "Noncausal Vector Autoregression," Econometric Theory, Cambridge University Press, vol. 29(03), pages 447-481, June.
  16. D.S. Poskitt & Wenying Yao, 2012. "VAR Modeling and Business Cycle Analysis: A Taxonomy of Errors," Monash Econometrics and Business Statistics Working Papers 11/12, Monash University, Department of Econometrics and Business Statistics.
  17. Paul Beaudry & Patrick Fève & Alain Guay & Franck Portier, 2015. "When is Nonfundamentalness in VARs a Real Problem? An Application to News Shocks," NBER Working Papers 21466, National Bureau of Economic Research, Inc.
  18. Forni, Mario & Gambetti, Luca, 2014. "Sufficient information in structural VARs," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 124-136.
  19. Mario Forni & Luca Gambetti & Marco Lippi & Luca Sala, 2017. "Noisy News in Business Cycles," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(4), pages 122-152, October.
  20. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2011. "Non‐Fundamentalness in Structural Econometric Models: A Review," International Statistical Review, International Statistical Institute, vol. 79(1), pages 16-47, April.
  21. 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, May.
  22. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2007. "Assessing Structural VARs," NBER Chapters,in: NBER Macroeconomics Annual 2006, Volume 21, pages 1-106 National Bureau of Economic Research, Inc.
  23. Lanne Markku & Saikkonen Pentti, 2011. "Noncausal Autoregressions for Economic Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-32, October.
  24. Christopher Otrok & Andre Kurmann, 2011. "News Shocks and the Term Structure of Interest Rates: A Challenge for DSGE Models," 2011 Meeting Papers 426, Society for Economic Dynamics.
  25. Eric R. Sims, 2012. "News, Non-Invertibility, and Structural VARs," Working Papers 013, University of Notre Dame, Department of Economics, revised Jun 2012.
  26. Mertens, Elmar, 2012. "Are spectral estimators useful for long-run restrictions in SVARs?," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1831-1844.
  27. Franchi, Massimo & Vidotto, Anna, 2013. "A check for finite order VAR representations of DSGE models," Economics Letters, Elsevier, vol. 120(1), pages 100-103.
  28. De Graeve, Ferre & Westermark, Andreas, 2013. "Un-truncating VARs," Working Paper Series 271, Sveriges Riksbank (Central Bank of Sweden).
  29. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers CWP21/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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