Robust IV Inference with Clustering Dependence (Job Market Paper)
Abstract: Linear IV models with clustering dependence are widely used in empirical studies, while the common solution, the cluster covariance estimator, often produces undesirable inferential results, especially with weak instruments. In this paper I propose a method that is robust to both weak IV and (potentially heterogeneous) clustering dependence. The proposed method is based on the idea of Fama-MacBeth estimation, with group-level estimators being a truncated version of the unbiased IV estimator. Asymptotic validity is shown under both strong and weak IV sequences, as well as under general requirements. Simulation results indicate the method has good finite sample performance in both size and power. The proposed method is applied to study the effect of city compactness on population density.
Synthetic Control Inference for Staggered Adoption: Estimating the Dynamic Effects of Board Gender Diversity Policies
(with Shirley Lu), Dec 2019. [Matlab]
Estimation and Inference for Synthetic Control Methods with Spillover Effects
(with Connor Dowd), Feb 2019. [Matlab, R]
On the Empirical Content of the Beckerian Marriage Model
(with 67(2), 349–362.
Principal Component and Static Factor Analysis
(with Chris Gu and Yike Wang), in Macroeconomic Forecasting in the Era of Big Data ed. by Peter Fuleky, 2020, 229-266. Advanced Studies in Theoretical and Applied Econometrics, vol 52, Springer, Cham.
Work in Progress
Optimal Inference under Weak Identification (with Tetsuya Kaji)
This project studies the optimality of inference methods in the general formulation of weak identification in semiparametric models, i.e., when a parameter is weakly regular. Under weak identification, standard methods usually fail to deliver valid inference. We formulate a class of inference procedures that are robust to weak identification. We show that by considering the quotient space of the underlying regular parameter space, the efficiency results of van der Vaart (1991) can be extended to our case.
Inference for Dependent Data with Cluster Learning (with Christian Hansen, Damian Kozbur, and Lucciano Villacorta)
Abstract: This paper presents and analyzes an approach to inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by an observed dissimilarity measure over spatial indeces. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the partition into clusters is learned, a cluster-based inference procedure is applied to a statistical hypothesis test. The procedure proposed in the paper allows the number of clusters to depend on the data, which gives researchers a principled method for choosing an appropriate clustering level. The paper gives conditions under which the proposed procedure asymptotically attains correct size.