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Assessing Structural VARs

In: NBER Macroeconomics Annual 2006, Volume 21

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  • Lawrence J. Christiano
  • Martin Eichenbaum
  • Robert Vigfusson

Abstract

This paper analyzes the quality of VAR-based procedures for estimating the response of the economy to a shock. We focus on two key issues. First, do VAR-based confidence intervals accurately reflect the actual degree of sampling uncertainty associated with impulse response functions? Second, what is the size of bias relative to confidence intervals, and how do coverage rates of confidence intervals compare with their nominal size? We address these questions using data generated from a series of estimated dynamic, stochastic general equilibrium models. We organize most of our analysis around a particular question that has attracted a great deal of attention in the literature: How do hours worked respond to an identified shock? In all of our examples, as long as the variance in hours worked due to a given shock is above the remarkably low number of 1 percent, structural VARs perform well. This finding is true regardless of whether identification is based on short-run or long-run restrictions. Confidence intervals are wider in the case of long-run restrictions. Even so, long-run identified VARs can be useful for discriminating among competing economic models.

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This chapter was published in:

  • Daron Acemoglu & Kenneth Rogoff & Michael Woodford, 2007. "NBER Macroeconomics Annual 2006, Volume 21," NBER Books, National Bureau of Economic Research, Inc, number acem06-1, October.
    This item is provided by National Bureau of Economic Research, Inc in its series NBER Chapters with number 11177.

    Handle: RePEc:nbr:nberch:11177

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    1. Christopher Erceg & Luca Guerrieri & Christopher Gust, 2004. "Can long-run restrictions identify technology shocks?," International Finance Discussion Papers 792, Board of Governors of the Federal Reserve System (U.S.).
    2. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, 09.
    3. Pierre-Daniel G. Sarte, 1997. "On the identification of structural vector autoregressions," Economic Quarterly, Federal Reserve Bank of Richmond, issue Sum, pages 45-68.
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    1. Matching Theory and Data: Bayesian Vector Autoregression and Dynamic Stochastic General Equilibrium Models
      by Christian Zimmermann in NEP-DGE blog on 2009-09-27 01:45:04
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    1. Advanced Monetary Theory and Policy (ECON 447)

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