About the Speaker:


Qu Feng is a PhD candidate from Syracuse University. His areas of interest are in Panel Data, Spatial Econometrics, Time Series, and productivity analysis. He has published a paper in Economic Letters.

Abstract:

This paper proposes a new test for cross-sectional dependence in fixed effects 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 specifications 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 fixed effects regression are used. As shown in the paper, the difference cannot be ignored asymptotically, and the limiting distribution of the proposed test statistic is derived. Additionally, its finite sample properties are examined and compared to the traditional tests for cross-sectional dependence using Monte Carlo simulations.  

Reservation:

Admission is free.  Please reply to Christina, e-mail: achristina@ntu.edu.sg or Tel: 6790-5689 to confirm your attendance.