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Open Access Publications from the University of California

Recent Work

The department was founded in 1964 and has 35 permanent members. We are a relatively young group, all committed to a rigorous analytical approach to both teaching and research. As a consequence, we have a congenial and cooperative atmosphere in which department members take an unusually active interest in their colleagues' research. There are no social or administrative distinctions between junior and senior faculty, except on promotion decisions. Eight faculty members are Fellows of the Econometric Society, three are on the Econometric Society Council, and three are Fellows of the American Academy of Arts and Sciences. Five are NBER Research Associates, and twelve have NSF grants.

University of California, San Diego
9500 Gilman Drive
La Jolla, CA 92093-0508
USA

Detecting p-Hacking

(2022)

We theoretically analyze the problem of testing for p‐hacking based on distributions of p‐values across multiple studies. We provide general results for when such distributions have testable restrictions (are non‐increasing) under the null of no p‐hacking. We find novel additional testable restrictions for p‐values based on t‐tests. Specifically, the shape of the power functions results in both complete monotonicity as well as bounds on the distribution of p‐values. These testable restrictions result in more powerful tests for the null hypothesis of no p‐hacking. When there is also publication bias, our tests are joint tests for p‐hacking and publication bias. A reanalysis of two prominent data sets shows the usefulness of our new tests.

Cover page of Location-Scale and Compensated Effects in Unconditional Quantile Regressions

Location-Scale and Compensated Effects in Unconditional Quantile Regressions

(2022)

This paper proposes an extension of the unconditional quantile regression analysis to (i) location-scale shifts, and (ii) compensated shifts. The first case is intended to study a counterfactual policy analysis aimed at increasing not only the mean or location of a covariate but also its dispersion or scale. The compensated shift refers to a situation where a shift in a covariate is compensated at a certain rate by another covariate. Not accounting for these possible scale or compensated effects will result in an incorrect assessment of the potential policy effects on the quantiles of an outcome variable. More general interventions and compensated shifts are also considered. The unconditional policy parameters are estimated with simple semiparametric estimators, for which asymptotic properties are studied. Monte Carlo simulations are implemented to study their finite sample performances, and the proposed approach is applied to a Mincer equation to study the effects of a location scale shift in education on the unconditional quantiles of wages.

Distributional conformal prediction.

(2021)

We propose a robust method for constructing conditionally valid prediction intervals based on models for conditional distributions such as quantile and distribution regression. Our approach can be applied to important prediction problems, including cross-sectional prediction, k-step-ahead forecasts, synthetic controls and counterfactual prediction, and individual treatment effects prediction. Our method exploits the probability integral transform and relies on permuting estimated ranks. Unlike regression residuals, ranks are independent of the predictors, allowing us to construct conditionally valid prediction intervals under heteroskedasticity. We establish approximate conditional validity under consistent estimation and provide approximate unconditional validity under model misspecification, under overfitting, and with time series data. We also propose a simple "shape" adjustment of our baseline method that yields optimal prediction intervals.

Omitted Variable Bias of Lasso-Based Inference Methods: A Finite Sample Analysis

(2021)

Abstract We study the finite sample behavior of Lasso-based inference methods such as post double Lasso and debiased Lasso. We show that these methods can exhibit substantial omitted variable biases (OVBs) due to Lasso not selecting relevant controls. This phenomenon can occur even when the coeffcients are sparse and the sample size is large and larger than the number of controls. Therefore, relying on the existing asymptotic inference theory can be problematic in empirical applications. We compare the Lasso-based inference methods to modern highdimensional OLS-based methods and provide practical guidance.

Cover page of Theoretical Foundations of Relational Incentive Contracts

Theoretical Foundations of Relational Incentive Contracts

(2021)

This article describes the emerging game-theoretic framework for modeling long-term contractual relationships with moral hazard. The framework combines self-enforcement and external enforcement, accommodating alternative assumptions regarding how actively the parties initially set and renegotiate the terms of their contract. A progression of theoretical components is reviewed, building from the recursive formulation of equilibrium continuation values in repeated games. A principal-agent setting serves as a running example.

Cover page of Political Alignment, Attitudes Toward Government, and Tax Evasion

Political Alignment, Attitudes Toward Government, and Tax Evasion

(2021)

We ask whether attitudes toward government play a causal role in the evasion of US personal income taxes. As turnover elections move voters in partisan counties into and out of alignment with the party of the president, we find with alignment (i) taxpayers report more easily evaded forms of income; (ii) suspect EITC claims decrease; and (iii) audits triggered and audits found to owe additional tax decrease. Coupled with evidence that alignment leads to more favorable views on taxation and spending, our results provide real world evidence that a positive outlook on government lowers tax evasion. (JEL D72, H24, H26, H31)

Cover page of Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment:  an Unconditional MTE Approach

Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment:  an Unconditional MTE Approach

(2021)

This paper studies the identification and estimation of unconditional policy effects when the treatment is binary and endogenous. We first characterize the asymptotic bias of the unconditional regression estimator that ignores the endogeneity and elaborate on the channels that the endogeneity can render the unconditional regressor estimator inconsistent. We show that even if the treatment status is exogenous, the unconditional regression estimator can still be inconsistent when there are common covariates affecting both the treatment status and the outcome variable. We introduce a new class of marginal treatment effects (MTE) based on the influence function of the functional underlying the policy target. We show that an unconditional policy effect can be represented as a weighted average of the newly defined MTEs over the individuals at the margin of indifference. Point identification is achieved using the local instrumental variable approach. Furthermore, the unconditional policy effects are shown to include the marginal policy-relevant treatment effect in the literature as a special case. Methods of estimation and inference for the unconditional policy effects are provided. In the empirical application, we estimate the effect of changing college enrollment status, induced by higher tuition subsidy, on the quantiles of the wage distribution.