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Testing for cross-sectional dependence in fixed e¤ects panel data models (2009).
| Content Provider | CiteSeerX |
|---|---|
| Author | Baltagi, Badi H. Feng, Qu Kao, Chihwa |
| Abstract | This paper proposes a new test for cross-sectional dependence in …xed e¤ects panel data models. It is well known that ignoring cross-sectional dependence leads to incorrect statistical inference. In the panel data literature, attempts to account for cross-sectional dependence include factor models and spatial correlation. In most cases, strong assumptions on the covariance matrix are imposed. Attempts at avoiding ad hoc speci…cations rely on the sample covariance matrix. Unfortunately, when the dimension of this variance-covariance matrix is large, the sample covariance matrix turns out to be an inconsistent estimator of the population covariance matrix. This is especially relevant for micro panels with a large number of cross-sectional units observed over a short time series span. This fact undermines existing tests based on the sample covariance matrix directly. This paper uses the Random Matrix Theory based approach of Ledoit and Wolf (2002) to test for cross-sectional dependence of the error terms in linear large panel models with comparable number of cross-sectional units and time series observations. Since the errors are unobservable, the residuals from the …xed e¤ects regression are used. As shown in the paper, the di¤erence can not be ignored asymptotically, and the limiting distribution of the proposed test statistic is derived. Additionally, its …nite sample properties are examined and compared to the traditional tests for cross-sectional dependence using Monte Carlo simulations. |
| File Format | |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | Cross-sectional Dependence Sample Covariance Matrix Panel Data Model Cross-sectional Unit Xed Ect Panel Data Model Factor Model Time Series Observation Test Statistic Traditional Test Large Number New Test Short Time Series Span Nite Sample Property Linear Large Panel Model Monte Carlo Simulation Micro Panel Panel Data Literature Di Erence Strong Assumption Ad Hoc Speci Cation Random Matrix Theory Limiting Distribution Covariance Matrix Comparable Number Population Covariance Matrix Inconsistent Estimator Variance-covariance Matrix Error Term Xed Ect Regression Statistical Inference Spatial Correlation |
| Content Type | Text |
| Resource Type | Article |
