47 papers
While acknowledging the ethical motivation behind a proposed "Hippocratic" utility function that prioritizes saving lives by avoiding harmful treatments over saving them through intervention, the author argues that the practical scope of this decision criterion is limited, as demonstrated by a counterexample.
This paper develops self-normalization-based structural break tests for predictive quantile and CoVaR regressions that remain valid regardless of predictor stationarity, thereby enabling robust detection of changes in forecasting power for risk and systemic risk models.
This paper employs stochastic modeling and empirical analysis of Chinese provincial data to demonstrate how monetary policy, innovation, and labor market fluctuations interact with heterogeneous shocks to shape international trade dynamics, revealing that optimal strategies should prioritize dynamic trade pattern transitions over static comparative advantages.
This paper provides a formal proof that the Quasi-Maximum Likelihood Estimator for binary choice models yields a consistent estimate of the slope coefficient under conditions similar to those identified by Manski, thereby resolving a long-standing gap in the theoretical justification for using logistic regression in such contexts.
This paper proposes a robust identification approach to derive sharp bounds on the causal effects of school assignment despite strategic preference misreporting, demonstrating significant heterogeneity in graduation outcomes using data from Chile's university matching system.
This paper develops Generalized Method of Moments (GMM) and M estimators for network-dependent data by establishing a novel uniform law of large numbers that ensures their consistency and asymptotic normality, while also providing complete procedures for practical estimation and inference.
This paper proposes a consistent, moment-based Bayesian testing procedure using exponentially tilted empirical likelihood to detect endogeneity in linear regression models, demonstrating its effectiveness through simulations and real-world applications involving automobile and airline markets.
This paper proposes a unified calibration framework that integrates heterogeneous internal and auxiliary information into randomized experiments under covariate-adaptive randomization via convex optimization, ensuring asymptotic validity and a no-harm efficiency guarantee while accommodating scenarios with growing numbers of strata and information sources.
This paper proposes a structured semiparametric framework combining an algorithm choice model and a viewer response model to correct the severe bias in standard estimators caused by algorithmic interference in two-sided marketplaces, thereby enabling accurate global treatment effect estimation for platform-wide algorithm updates.
This paper introduces the TEA-Time framework for extrapolating treatment effects across different time periods by proposing two identification strategies with doubly robust estimators, which are validated through simulations and applied to Upworthy A/B tests to demonstrate a trade-off between precision and bias.
This paper proposes a new confidence interval for the autoregressive parameter that maintains robust coverage regardless of the initial condition and conditional heteroskedasticity, while incurring only a minimal cost in interval length when standard assumptions hold.
This paper introduces a modified conditional sum-of-squares (MCSS) estimator for ARFIMA models that corrects the bias caused by estimating a constant term, demonstrating through theoretical analysis and simulations that it significantly outperforms the standard CSS estimator even in small samples, and applying this improvement to reanalyze three classical economic and hydrological datasets.
This paper provides an analytical characterization of the Synthetic Control Method's degrees of freedom to derive estimable information criteria that improve model selection over cross-validation, particularly in settings with noisy donors and numerous candidates such as the Tianjin car license rationing case.
This paper introduces Gimbal Regression, a deterministic and geometry-aware local regression framework that ensures stable, auditable estimation of spatial heterogeneity by constructing directional weights from neighborhood geometry to mitigate numerical instability caused by anisotropic or low-dimensional data structures.
This paper proposes a novel operator-level ARCH framework for data in general separable Hilbert spaces that models the full evolution of the conditional covariance operator, establishing conditions for stationarity and deriving consistent estimators, which are validated through simulations and an application to high-frequency intraday returns.
This paper proposes a modified Double Machine Learning estimator featuring "Reverse Cross-Fitting" and a specialized calibration rule to enable robust causal inference in high-dimensional macroeconomic time series, demonstrating its effectiveness through simulations and an application to the dynamic effects of Tier 1 regulatory capital.
This paper proposes a shrinkage regularization-based portmanteau test to detect both linear and nonlinear serial dependence in high-dimensional non-Gaussian time series, extending the method introduced by Jasiak and Neyazi (2023) to accommodate high-dimensional settings.
This paper establishes a unified theory showing that the minimax optimal estimation rate for functionals identified by "thin sets" (submanifolds of dimension in a -dimensional space) depends critically on the intrinsic dimensionality, specifically achieving a rate of for nonparametric functions with smoothness , and provides valid inference procedures via sieve Riesz representation and Sobol points.
This paper establishes a novel connection between robust bias correction (RBC) and bootstrap prepivoting to develop an improved nonparametric inference procedure that produces confidence intervals 17% shorter than conventional methods without compromising asymptotic coverage.
This paper proposes a computationally tractable, correctly centered internal instrument IV estimator for linear regression models with clustered data, high-dimensional controls, and complex exclusion restrictions, providing robust inference procedures illustrated through a study of spatial interference in rural Kenya.