Toru Kitagawa, Simone Martinalli
arXiv 19 Oct 2026 · Econometrics
arXiv:2610.27405 · PDF · Extracted main text
This paper studies data-driven macroeconomic policy choice in a structural VAR framework. We formulate the policymaker's problem of optimizing the future path of a policy instrument as a statistical decision problem under two alternative policy regimes: a "contemporaneous" regime, where the chosen path is implemented through unexpected policy shocks each period, and a "forward guidance" regime, where the expected path is announced in advance and enforced through anticipated policy shocks. We derive oracle, Bayes-optimal, and robust Bayes decision rules, the last for the case where structural impulse responses are only set-identified, and show that the decision problem depends on the structural rotation matrix only through the span of the columns associated with the policy shocks. We argue that the plug-in rule is uniformly dominated by a James-Stein-type shrinkage rule in a local asymptotic Gaussian limit experiment, and is therefore inadmissible therein. Numerical analysis calibrated to monetary SVARs compares the risks of the plug-in and a benchmark Bayes rule in Monte Carlo Gaussian simulations and finds that the Bayes rule attains lower risk at 95.1-99.9 percent of the posterior draws in the two regimes. We then study the relationship between the one-shot decision for a policy path and sequential policymaking, and show that, in the contemporaneous regime, the one-shot oracle path coincides with the expectation of the sequentially optimal oracle plan; we document that this representativeness property fails under Bayesian decision-making with parameter uncertainty. In an application to U.S. monetary policy in 2006-2007, we find that the plug-in and Bayes rules can differ substantially: the Bayes-optimal rule prescribes an initial tightening followed by partial easing across different specifications, while the plug-in changes across regimes.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Giacomini, Raffaella and Kitagawa, Toru (2021) Robust Bayesian inference for set-identified models self | 1.000 | 7 | 4 | 100% |
| 2 | Sims, Christopher A. and Zha, Tao (2006) Does monetary policy generate recessions? | 1.000 | 7 | 3 | 100% |
| 3 | McKay, Alisdair and Wolf, Christian K (2023) What can time-series regressions tell us about policy counterfactuals? | 0.976 | 14 | 5 | 93% |
| 4 | Romer, Christina D. and Romer, David H (2004) A new measure of monetary shocks: Derivation and implications | 0.928 | 5 | 3 | 80% |
| 5 | Barnichon, Régis and Mesters, Geert (2023) A sufficient statistics approach for macro policy | 0.894 | 14 | 6 | 71% |
| 6 | Giacomini, Raffaella and Kitagawa, Toru and Read, Matthew (2026) Robust Bayesian analysis for econometrics self | 0.874 | 5 | 2 | 100% |
| 7 | Antolin-Diaz, Juan and Petrella, Ivan and Rubio-Ram\'irez, Juan F (2021) Structural scenario analysis with SVARs | 0.843 | 10 | 5 | 60% |
| 8 | Leeper, Eric M. and Zha, Tao (2003) Modest policy interventions | 0.843 | 3 | 3 | 100% |
| 9 | Theil, Henri (1957) A note on certainty equivalence in dynamic planning | 0.737 | 5 | 3 | 40% |
| 10 | Bertsekas, Dimitri P (1995) Dynamic programming and optimal control | 0.644 | 5 | 2 | 40% |
Showing the top 10 of 75 scored citations.