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Title: Testing overidentifying restrictions with high-dimensional data and heteroskedasticity Authors:  Qingliang Fan - The Chinese University of Hong Kong (Hong Kong)
Zijian Guo - Rutgers University (United States)
Ziwei Mei - The Chinese University of Hong Kong (Hong Kong) [presenting]
Abstract: A new test is proposed for overidentifying restrictions (called the Q test) with high-dimensional data. This test is based on estimation and inference for quadratic forms of high-dimensional parameters. It is shown to have the desired asymptotic size and power properties under heteroskedasticity, even if the number of instruments and covariates is larger than the sample size. Simulation results show that the new test performs favorably compared to existing alternative tests under the scenarios when those tests are feasible or not. An empirical example of the trade and economic growth nexus manifests the usefulness of the proposed test.