Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/211107
Authors: 
Moon, Hyungsik Roger
Weidner, Martin
Year of Publication: 
2019
Series/Report no.: 
cemmap working paper No. CWP14/19
Abstract: 
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. Those estimators have a very important computational advantage compared to the existing least squares (LS) estimator, in that they are de fined as minimizers of a convex objective function. In addition, the nuclear norm penalization helps to resolve a potential identifi cation problem for interactive fixed effect models, in particular when the regressors are low-rank and the number of the factors is unknown. 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 nite 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.
Subjects: 
Interactive Fixed Effects
Factor Models
Nuclear Norm
Document Type: 
Working Paper

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