Macroeconomic Responses to Uncertainty Shocks: The Perils of Recursive Orderings

ABSTRACT A common practice in empirical macroeconomics is to examine alternative recursive orderings of the variables in structural vector autoregressive (VAR) models. When the implied impulse responses look similar, the estimates are considered trustworthy. When they do not, the estimates are used to bound the true response without directly addressing the identification challenge. A leading example of this practice is the literature on the effects of uncertainty shocks on economic activity. We prove by counterexample and show by simulation that this practice is invalid, whether the data generating process is a structural VAR model or a dynamic stochastic general equilibrium model. Simulation evidence suggests that the underlying identification challenge can be addressed using an instrumental variables estimator.

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Publication Details

Journal
Journal of Applied Econometrics
Published
2025-02-06
DOI
https://doi.org/10.1002/jae.3113
Citations
6
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
18.79
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article

Macroeconomic Responses to Uncertainty Shocks: The Perils of Recursive Orderings

Lutz Kilian, Michael Plante, Alexander W. Richter
6 citations
Journal of Applied Econometrics
Market Dynamics and Volatility
18.79
article

Macroeconomic Responses to Uncertainty Shocks: The Perils of Recursive Orderings

Lutz Kilian, Michael Plante, Alexander W. Richter
article en
6 citations

Abstract

ABSTRACT A common practice in empirical macroeconomics is to examine alternative recursive orderings of the variables in structural vector autoregressive (VAR) models. When the implied impulse responses look similar, the estimates are considered trustworthy. When they do not, the estimates are used to bound the true response without directly addressing the identification challenge. A leading example of this practice is the literature on the effects of uncertainty shocks on economic activity. We prove by counterexample and show by simulation that this practice is invalid, whether the data generating process is a structural VAR model or a dynamic stochastic general equilibrium model. Simulation evidence suggests that the underlying identification challenge can be addressed using an instrumental variables estimator.

Journal of Applied EconometricsVol. 40(4)
Center for Economic and Policy Research (US), Federal Reserve Bank of Dallas (US)
Decent work and economic growth
Openalex Percentile: Top 1%
Market Dynamics and Volatility
18.79
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