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Full-Text Articles in Econometrics
The Chow Test With Time Series-Cross Section Data, James K. Binkley, Jeffrey Young
The Chow Test With Time Series-Cross Section Data, James K. Binkley, Jeffrey Young
Faculty & Staff Research and Creative Activity
The Chow test is a standard method to test for differences in regression response across groups. In some cases, the groups being tested are composed of a time series of cross sections. If the individual units within the groups have systematic differences, the Chow test is compromised: individual and group effects become confounded. This can cause rejections in the absence of the group effect of interest. We illustrate the problem with Monte Carlo analyses, and propose an alternative bootstrap-like testing procedure that helps eliminate excessive Type I errors.
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Yichong Zhang, Xin Zheng
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Yichong Zhang, Xin Zheng
Research Collection School Of Economics
In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard …