Social and Behavioral Sciences Commons^™

Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su

Research Collection School Of Economics

In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to …

Essays In Labor Economics And Panel Data Analysis, Evan S. Totty

Open Access Dissertations

This dissertation is composed of three independent chapters. The first chapter studies the impact of minimum wage hikes on employment of low-skill workers in the United States. The second chapter studies the lasting impact of attending a higher value-added high school on college performance. The third chapter studies the impact of stuttering on labor market outcomes.

The first chapter resolves issues in the minimum wage-employment debate by using factor model econometric methods to address concerns related to unobserved heterogeneity. Recent work has shown that the negative effects of minimum wages on employment found using traditional methods are sensitive to the …

Shrinkage Estimation Of Dynamic Panel Data Models With Interactive Fixed Effects, Xun Lu, Liangjun Su

Research Collection School Of Economics

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai (2009) and Moon and Weidner (2015), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to zero. …