What Can Time-Series Regressions Tell Us About Policy Counterfactuals?
We show that, in a general family of linearized structural macroeconomic models, knowledge of the empirically estimable causal effects of contemporaneous and news shocks to the prevailing policy rule is sufficient to construct counterfactuals under alternative policy rules. If the researcher is willing to postulate 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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Copy CitationChristian K. Wolf and Alisdair McKay, "What Can Time-Series Regressions Tell Us About Policy Counterfactuals?," NBER Working Paper 30358 (2022), https://doi.org/10.3386/w30358.Download Citation
Published Versions
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. citation courtesy of 
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