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.
In many macroeconomic applications, impulse responses and their frequentist confidence intervals are constructed by estimating a VAR model in levels - thus ignoring uncertainty regarding the true (unknown) cointegration rank. In this paper we investigate the consequences of ignoring this uncertainty. We adapt several proposed methods for handling model uncertainty to perform inference in cointegrated VAR models and highlight their shortcomings in the present setting. Therefore, we propose a new method - Weighted Inference by Model Plausibility (WIMP) - that takes rank uncertainty into account in a fully data-driven way . In a simulation study the WIMP method outperforms all other methods considered , delivering intervals that are robust to rank uncertainty, yet not overly conservative. We also 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 . 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.
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(bootstrap)
frequentist
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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
In this paper we investigate the consequences of ignoring this uncertainty. We adapt several proposed methods
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to perform inference in cointegrated VAR models and highlight their shortcomings in the present setting. Therefore, we
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in the simulation study. The WIMP method is shown to deliver
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allow for meaningful inference, approaching fixed rank intervals when evidence for a particular rank is strong. We
not overly conservative. We also
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that have been considered in the literature