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Impulse-Based Computation of Policy Counterfactuals

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Abstract

We propose an efficient procedure to solve for policy counterfactuals in linear models with occasionally binding constraints. The procedure does not require knowledge of the structural or reduced-form equations of the model, its state variables, or its shock processes. Forecasts of the variables entering the policy problem, and impulse response functions of these variables to anticipated policy shocks under an arbitrary policy, constitute sufficient information to construct valid counterfactuals. We show how to compute solutions for instrument rules and optimal discretionary and commitment policies with multiple policy instruments, and discuss various extensions including imperfect information, asymmetric objectives, and limited commitment. Our procedure facilitates the comparison of the effects of policy regimes across models. As an application, we compute counterfactual paths of the US economy around 2015 for several monetary policy regimes.

Suggested Citation

  • James Hebden & Fabian Winkler, 2021. "Impulse-Based Computation of Policy Counterfactuals," Finance and Economics Discussion Series 2021-042, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2021-42
    DOI: 10.17016/FEDS.2021.042
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    1. Lars E.O. Svensson & Robert J. Tetlow, 2005. "Optimal Policy Projections," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
    2. Tom D. Holden, 2023. "Existence and Uniqueness of Solutions to Dynamic Models with Occasionally Binding Constraints," The Review of Economics and Statistics, MIT Press, vol. 105(6), pages 1481-1499, November.
    3. Lars E O Svensson, 2005. "Monetary Policy with Judgment: Forecast Targeting," International Journal of Central Banking, International Journal of Central Banking, vol. 1(1), May.
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    5. Bersson, Betsy & Hürtgen, Patrick & Paustian, Matthias, 2019. "Expectations formation, sticky prices, and the ZLB," Discussion Papers 34/2019, Deutsche Bundesbank.
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    8. Dennis, Richard, 2007. "Optimal Policy In Rational Expectations Models: New Solution Algorithms," Macroeconomic Dynamics, Cambridge University Press, vol. 11(1), pages 31-55, February.
    9. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    10. Holden, Tom D., 2016. "Existence, uniqueness and computation of solutions to dynamic models with occasionally binding constraints," EconStor Preprints 127430, ZBW - Leibniz Information Centre for Economics.
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    Cited by:

    1. Alisdair McKay & Christian K. Wolf, 2023. "What Can Time‐Series Regressions Tell Us About Policy Counterfactuals?," Econometrica, Econometric Society, vol. 91(5), pages 1695-1725, September.
    2. Alexandre Carrier & Kostas Mavromatis, 2024. "Optimal normalization policy under behavioral expectations," Working Papers 800, DNB.

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    More about this item

    Keywords

    Computation; DSGE; Occasionally Binding Constraints; Optimal Policy; Commitment; Discretion;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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