Selected work

Representative Papers

Asymptotic Properties of Empirical Quantile-Based Estimators

Jun 30, 2026

This study addresses the estimation of parameters of the form θ₀ = E[F_Y⁻¹∘F_Z(X)] in the “changes-in-changes” model, for which existing methods lack theoretical guarantees when variables are unbounded. The authors construct a plug-in estimator based on empirical quantiles and establish its √n-consistency and asymptotic normality under assumptions weaker than those in the current literature. They further propose a novel consistent estimator for the asymptotic variance. The theoretical analysis leverages empirical process theory and plug-in methods for quantile functions. Monte Carlo simulations demonstrate that the proposed variance estimator substantially outperforms existing alternatives, leading to markedly improved inference accuracy.

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Treatment-Effect Estimation in Complex Designs under a Parallel-trends Assumption

Aug 11, 2025

This paper addresses the identification of dynamic treatment effects in panel data with non-binary, non-absorbing treatments. Under no-anticipation and generalized parallel-trends assumptions, it identifies event-study effects by contrasting observed treatment paths with a “maintain-initial-state” counterfactual path. It further proposes a random-coefficient distributed-lag model to estimate the marginal dynamic policy impact. Unlike conventional two-way fixed-effects estimators—which impose restrictive assumptions on treatment timing and absorption—the method cleanly separates actual policy effects from extrapolated counterfactuals under unimplemented policies. Integrating regression adjustment with weight normalization, the approach is empirically validated using Gentzkow et al. (2011) data, accurately recovering both immediate and lagged effects. The framework enhances interpretability and applicability for evaluating complex, evolving policies, particularly those featuring gradual, reversible, or heterogeneous treatment adoption.

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Recent publications

Latest Papers

Asymptotic Properties of Empirical Quantile-Based Estimators

Jun 30, 2026

This study addresses the estimation of parameters of the form θ₀ = E[F_Y⁻¹∘F_Z(X)] in the “changes-in-changes” model, for which existing methods lack theoretical guarantees when variables are unbounded. The authors construct a plug-in estimator based on empirical quantiles and establish its √n-consistency and asymptotic normality under assumptions weaker than those in the current literature. They further propose a novel consistent estimator for the asymptotic variance. The theoretical analysis leverages empirical process theory and plug-in methods for quantile functions. Monte Carlo simulations demonstrate that the proposed variance estimator substantially outperforms existing alternatives, leading to markedly improved inference accuracy.

0 citationsRead paper

Treatment-Effect Estimation in Complex Designs under a Parallel-trends Assumption

Aug 11, 2025

This paper addresses the identification of dynamic treatment effects in panel data with non-binary, non-absorbing treatments. Under no-anticipation and generalized parallel-trends assumptions, it identifies event-study effects by contrasting observed treatment paths with a “maintain-initial-state” counterfactual path. It further proposes a random-coefficient distributed-lag model to estimate the marginal dynamic policy impact. Unlike conventional two-way fixed-effects estimators—which impose restrictive assumptions on treatment timing and absorption—the method cleanly separates actual policy effects from extrapolated counterfactuals under unimplemented policies. Integrating regression adjustment with weight normalization, the approach is empirically validated using Gentzkow et al. (2011) data, accurately recovering both immediate and lagged effects. The framework enhances interpretability and applicability for evaluating complex, evolving policies, particularly those featuring gradual, reversible, or heterogeneous treatment adoption.

0 citationsRead paper