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Inference for Impulse Responses under Model Uncertainty

Lenard Lieb () and Stephan Smeekes ()

No 22, Research Memorandum from Maastricht University, Graduate School of Business and Economics (GSBE)

Abstract: In many macroeconomic applications, impulse responses and their (bootstrap) confidence intervals are constructed by estimating a VAR model in levels - thus ignoring uncertainty regarding the true (unknown) cointegration rank. While it is well known that using a wrong cointegration rank leads to invalid (bootstrap) inference, we demonstrate that even if the rank is consistently estimated, ignoring uncertainty regarding the true rank can make inference highly unreliable for sample sizes encountered in macroeconomic applications. We investigate the effects of rank uncertainty in a simulation study, comparing several methods designed for handling model uncertainty. We propose a new method - Weighted Inference by Model Plausibility (WIMP) - that takes rank uncertainty into account in a fully data-driven way and outperforms all other methods considered in the simulation study. The WIMP method is shown to deliver intervals that are robust to rank uncertainty, yet allow for meaningful inference, approaching fixed rank intervals when evidence for a particular rank is strong. We study the potential ramifications of rank uncertainty on applied macroeconomic analysis by re-assessing the effects of fiscal policy shocks based on a variety of identification schemes that have been considered in the literature. We demonstrate how sensitive the results are to the treatment of the cointegration rank, and show how formally accounting for rank uncertainty can affect the conclusions.

Keywords: Impulse response analysis; cointegration; model uncertainty; bootstrap inference; fiscal policy shocks (search for similar items in EconPapers)
JEL-codes: C15 C32 C52 E62 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-mac
Date: 2017-10-03
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