Assessing Structural VARs
In: NBER Macroeconomics Annual 2006, Volume 21
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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- Christopher J. Erceg & Luca Guerrieri, 2004.
"Can Long-Run Restrictions Identify Technology Shocks?,"
Computing in Economics and Finance 2004
3, Society for Computational Economics.
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- 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.).
- Frank Smets & Raf Wouters, 2003.
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- 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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