Computation of Policy Counterfactuals in Sequence Space

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.