Computation of Policy Counterfactuals in Sequence Space

James Hebden and Fabian Winkler

No 2021-042r1, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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.

Keywords: Sequence space; DSGE; Occasionally Binding Constraints; Optimal Policy; Commitment; Discretion (search for similar items in EconPapers)
JEL-codes: C61 C63 E52 (search for similar items in EconPapers)
Pages: 51 p.
Date: 2021-07-15, Revised 2024-09-01
New Economics Papers: this item is included in nep-cba, nep-cmp, nep-dge, nep-mac and nep-ore
Note: Revision
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2021-42

DOI: 10.17016/FEDS.2021.042r1

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