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Social and Behavioral Sciences Commons™
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Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
On Testing For Sphericity With Non-Normality In A Fixed Effects Panel Data Model, Badi H. Baltagi, Chihwa Kao, Bin Peng
On Testing For Sphericity With Non-Normality In A Fixed Effects Panel Data Model, Badi H. Baltagi, Chihwa Kao, Bin Peng
Center for Policy Research
Building upon the work of Chen et al. (2010), this paper proposes a test for sphericity of the variance-covariance matrix in a fixed effects panel data regression model without the normality assumption on the disturbances.
Test Of Hypotheses In A Time Trend Panel Data Model With Serially Correlated Error Component Disturbances, Chihwa Kao, Badi H. Baltagi, Long Liu
Test Of Hypotheses In A Time Trend Panel Data Model With Serially Correlated Error Component Disturbances, Chihwa Kao, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper studies test of hypotheses for the slope parameter in a linear time trend panel data model with serially correlated error component disturbances. We propose a test statistic that uses a bias corrected estimator of the serial correlation parameter. The proposed test statistic which is based on the corresponding fixed effects feasible generalized least squares (FE-FGLS) estimator of the slope parameter has the standard normal limiting distribution which is valid whether the remainder error is I(0) or I(1). This performs well in Monte Carlo experiments and is recommended.
Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach, Sung Jae Jun, Yoonseok Lee, Youngki Shin
Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach, Sung Jae Jun, Yoonseok Lee, Youngki Shin
Center for Policy Research
We propose the sharp identifiable bounds of the distribution functions of potential outcomes using a panel with fixed T. We allow for the possibility that the statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the bounds. Dynamics in the treatment decisions is allowed as long as the stationarity assumptions are satisfied. In particular, we present an example where our assumptions are satisfied and the treatment decision of the present time may depend on the treatments and the observed outcomes of the past. As an empirical illustration …