ALT We propose a test for essential heterogeneity in the marginal treatment effects model without imposing additive separability. On the observed support of the propensity score, no essential heterogeneity implies that the conditional mean of the outcome is affine in the propensity score, and this affine restriction is the sharp testable implication of the null. We implement the test using a projected Gaussian-process statistic that is Neyman-orthogonal to the first-step estimation error, so the multiplier bootstrap avoids re-estimating nuisance parameters. Monte Carlo experiments show accurate size control and good power, and, unlike the fixed-scale integrated conditional moment benchmark, the data-driven median rule retains power as the conditioning set grows in dimension. In an application to the Ugandan ELA programme, the omnibus statistic rejects the null of no essential heterogeneity for aspirations at the midline follow-up.

ALT This note contains a story and a paper. The paper is about the admissibility of two-stage least squares with weak instruments, a topic we have worked on together since 2019. The story is about how we (or, rather, our LLMs) wrote the paper, and some thoughts on what this may mean for our field.

ALT 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

ALT This paper studies inference on the value of a linear program (LP) when the objective and constraints are estimated. Using LP duality, we construct one- and two-sided confidence intervals by inverting tests of the feasibility of systems of linear inequalities. Existing tests yield asymptotically valid confidence intervals but can have poor power. We propose a modification that augments these systems with estimated “box constraints.” The resulting procedure has power against N⁻¹/²-local alternatives and yields confidence intervals of order N⁻¹/². These results require only that the LP value be finite, and do not require compactness of the feasible region nor standard constraint qualifications. We obtain stronger guarantees under various constraint qualifications and establish uniform validity over large classes of DGPs when known deterministic constraints bound the primal and/or dual variables. Simulations demonstrate good finite-sample performance.