Do Local Projections Solve the Bias Problem in Impulse Response Inference?
Lutz Kilian () and
Yun Jung Kim ()
No 7266, CEPR Discussion Papers from C.E.P.R. Discussion Papers
It is well documented that the small-sample accuracy of asymptotic and bootstrap approximations to the pointwise distribution of VAR impulse response estimators is undermined by the estimator’s bias. A natural conjecture is that impulse response estimators based on the local projection (LP) method of Jordà (2005, 2007) are less susceptible to this problem and hence potentially more reliable in small samples than VAR-based estimators. We show that - contrary to this conjecture - LP estimators tend to have both higher bias and higher variance, resulting in pointwise impulse response confidence intervals that are typically less accurate and wider on average than suitably constructed VAR-based intervals. Bootstrapping the LP estimator only worsens its finite-sample accuracy. We also evaluate recently proposed joint asymptotic intervals for VAR and LP impulse response functions. Our analysis suggests that the accuracy of joint intervals can be erratic in practice, and neither joint interval is uniformly preferred over the other.
Keywords: Bias; Confidence interval; Impulse response function; Joint interval; Local projection; Vector autoregression (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 (search for similar items in EconPapers)
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