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Computation of Policy Counterfactuals in Sequence Space

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Abstract

We propose an efficient procedure to solve for policy counterfactuals in linear models with occasionally binding constraints in sequence space. Forecasts of the variables relevant for the policy problem, and their impulse responses to anticipated policy shocks, constitute sufficient information to construct valid counterfactuals. Knowledge of the structural model equations or filtering of structural shocks is not required. We solve for deterministic and stochastic paths under instrument rules as well as under optimal policy with commitment or subgame-perfect discretion. As an application, we compute counterfactuals of the U.S. economy after the pandemic shock of 2020 under several monetary policy regimes.

Suggested Citation

  • James Hebden & Fabian Winkler, 2021. "Computation of Policy Counterfactuals in Sequence Space," Finance and Economics Discussion Series 2021-042r1, Board of Governors of the Federal Reserve System (U.S.), revised 01 Sep 2024.
    • Handle: RePEc:fip:fedgfe:2021-42
    DOI: 10.17016/FEDS.2021.042r1
    Note: Revision
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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. Carrier, Alexandre & Mavromatis, Kostas, 2025. "Optimal normalization policy under behavioral expectations," Journal of Monetary Economics, Elsevier, vol. 153(C).

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    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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