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A consistent nonparametric test of parametric regression functional form in fixed effects panel data models
Affiliation:1. Department of Economics, Texas A&M University, College Station, TX, 77843, USA;2. International School of Economics and Management, Capital University of Economics and Business, Beijing, 100070, PR China;3. Department of Economics, University of Guelph, Guelph, Ontario N1G 2W1, Canada;1. Department of Economics, National Chengchi University, Taipei 116, Taiwan;2. Department of Finance and CRETA, National Taiwan University, Taipei 106, Taiwan;1. Cowles Foundation for Research in Economics, Yale University, Box 208281, New Haven, CT 06520, USA;2. Department of Economics, UCLA, Box 951477, Los Angeles, CA 90095, USA;1. Cowles Foundation for Research in Economics, Yale University, Box 208281, New Haven, CT 06520, USA;2. Department of Economics, New York University, 19 West 4th Street, New York, NY 10012, USA;1. Department of Agricultural Economics and Rural Sociology, Auburn University, Auburn, AL, United States;2. Department of Economics and Finance, University of Guelph, ON, United States
Abstract:We propose a consistent test for a linear functional form against a nonparametric alternative in a fixed effects panel data model. We show that the test has a limiting standard normal distribution under the null hypothesis, and show that the test is a consistent test. We also establish the asymptotic validity of a bootstrap procedure which is used to better approximate the finite sample null distribution of the test statistic. Simulation results show that the proposed test performs well for panel data with a large number of cross-sectional units and a finite number of observations across time.
Keywords:Consistent test  Bootstrap  Fixed effects  Nonparametric estimation  Panel data
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