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Martin WEIDNER (University College London) – "Nuclear Norm Regularized Estimation of Panel Regression Models"
Time: 12:15 pm – 1:30 pm
Date: 27 th of november 2018
Place: Room 3001
Martin WEIDNER (University College London) – “Nuclear Norm Regularized Estimation of Panel Regression Models”
In this paper we investigate panel regression models with interactive fixed effects. We propose two new estimation methods that are based on minimizing convex objective functions. The first method minimizes the sum of squared residuals with a nuclear (trace) norm regularization. The second method minimizes the nuclear norm of the residuals. We establish the consistency of the two resulting estimators. We also show how to construct estimators that are asymptotically equivalent to the least squares (LS) estimator in Bai (2009) and Moon and Weidner (2017) by using our nuclear norm regularized or minimized estimators as initial values for a finite number of LS minimizing iteration steps. This iteration avoids any non-convex minimization, while the original LS estimation problem is generally non-convex, and can have multiple local minima. In addition to this computational advantage, our method also helps to consistently estimate regression coefficients on `low-rank’ regressors in the presence of an unknown number of factors, where the standard LS approach may give inconsistent estimates.
joint work with Hyungsik Roger Moon (USC).
Laurent DAVEZIES (Laboratoire de Microéconométrie-CREST)
Arne UHLENDORFF (Laboratoire de Microéconométrie-CREST)