Christian Wolf, Massachusetts Institute of Technology
What Can Time-Series Regressions Tell Us About Policy Counterfactuals?
Date and Location
Friday, November 4, 2022, 3:40 PM - 5:00 PM
Gold Room, 1131 Social Sciences and Humanities
We show that, in a general family of linearized structural macroe- conomic models, knowledge of the empirically estimable causal effects of contem- poraneous and news shocks to the prevailing policy rule is sufficient to construct counterfactuals under alternative policy rules. If the researcher is willing to pos- tulate a loss function, our results furthermore allow her to recover an optimal policy rule for that loss. Under our assumptions, the derived counterfactuals and optimal policies are robust to the Lucas critique. We then discuss strategies for applying these insights when only a limited amount of empirical causal evidence on policy shock transmission is available.
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