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Averaging Impulse Responses Using Prediction Pools

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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. Three Monte Carlo simulations and two monetary shock 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.

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  • Paul Ho & Thomas A. Lubik & Christian Matthes, 2023. "Averaging Impulse Responses Using Prediction Pools," Working Paper 23-04, Federal Reserve Bank of Richmond.
    • Handle: RePEc:fip:fedrwp:95601
    DOI: 10.21144/wp23-04
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    More about this item

    Keywords

    prediction pools; model averaging; impulse responses; misspecification;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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