Averaging impulse responses using prediction pools

Paul Ho, Thomas A. Lubik and Christian Matthes

Journal of Monetary Economics, 2024, vol. 146, issue C

Abstract: Macroeconomists construct impulse responses using many competing time series models and different statistical paradigms (Bayesian or frequentist). We adapt optimal linear prediction pools to efficiently combine impulse response estimators for the effects of the same economic shock from this vast class of possible models. We thus alleviate the need to choose one specific model, obtaining weights that are typically positive for more than one model. Our Monte Carlo simulations and empirical applications illustrate how the weights leverage the strengths of each model by (i) trading off properties of each model depending on variable, horizon, and application and (ii) accounting for the full predictive distribution rather than being restricted to specific moments.11MATLAB replication code is available on Matthes’ GitHub repository.

Keywords: Prediction pools; Model averaging; Impulse responses; Misspecification (search for similar items in EconPapers)
JEL-codes: C32 C52 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Working Paper: Averaging Impulse Responses Using Prediction Pools (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:moneco:v:146:y:2024:i:c:s0304393224000242

DOI: 10.1016/j.jmoneco.2024.103571

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