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Title: Estimation and inference for varying-coefficient multidimensional fixed-effects panel data models Authors:  Daniel Henderson - University of Alabama (United States) [presenting]
Christopher Parmeter - University of Miami (United States)
Alexandra Soberon - Universidad de Cantabria (Spain)
Abstract: A general estimation method is presented for a varying coefficient multidimensional panel data regression model and offers an array of hypothesis testing avenues. We derive the asymptotic distribution of our estimator, and to construct valid tests, we develop the necessary central limit theory to conduct inference. The presence of multiple effects over differing dimensions requires nontrivial changes to the central limit theory for U-statistics. The types of inference we can conduct offer a diverse array of hypotheses for applied work, and we explicitly present test statistics for some of the most important hypothesis tests. A detailed set of simulations supports our estimators asymptotic developments and reveals that our testing infrastructure possesses correct asymptotic size and high power.