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Un-truncating VARs

Author

Listed:
  • De Graeve, Ferre

    () (Research Department, Central Bank of Sweden)

  • Westermark, Andreas

    () (Research Department, Central Bank of Sweden)

Abstract

Macroeconomic research often relies on structural vector autoregressions to uncover empirical regularities. Critics argue the method goes awry due to lag truncation: short lag-lengths imply a poor approximation to DSGE-models. Empirically, short lag-length is deemed necessary as increased parametrization induces excessive uncertainty. The paper shows that this argument is incomplete. Longer lag-length simultaneously reduces misspecification, which in turn reduces variance. For data generated by frontier DSGE-models long-lag VARs are feasible, reduce bias and variance, and have better coverage. Thus, contrary to conventional wisdom, the trivial solution to the critique actually works.

Suggested Citation

  • De Graeve, Ferre & Westermark, Andreas, 2013. "Un-truncating VARs," Working Paper Series 271, Sveriges Riksbank (Central Bank of Sweden).
  • Handle: RePEc:hhs:rbnkwp:0271
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    File URL: http://www.riksbank.se/Documents/Rapporter/Working_papers/2013/rap_wp271_130626.pdf
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    References listed on IDEAS

    as
    1. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    2. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2004. "The Response of Hours to a Technology Shock: Evidence Based on Direct Measures of Technology," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 381-395, 04/05.
    3. Ravenna, Federico, 2007. "Vector autoregressions and reduced form representations of DSGE models," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2048-2064, October.
    4. 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.
    5. Faust, Jon & Leeper, Eric M, 1997. "When Do Long-Run Identifying Restrictions Give Reliable Results?," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 345-353, July.
    6. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88.
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    Cited by:

    1. Enders, Zeno & Kleemann, Michael & Müller, Gernot, 2013. "Growth expectations, undue optimism, and short-run fluctuations," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 80009, Verein für Socialpolitik / German Economic Association.
    2. Soccorsi, Stefano, 2016. "Measuring nonfundamentalness for structural VARs," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 86-101.

    More about this item

    Keywords

    VAR; SVAR; Lag-length; Truncation;

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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