Social and Behavioral Sciences Commons^™

Nonstationary Panels With Unobserved Heterogeneity, Wenxin Huang

Dissertations and Theses Collection (Open Access)

This dissertation develops several econometric techniques to address the unobserved heterogeneity in nonstationary panels, namely identifying latent group structures in cointegrated panels, studying nonstationary panels with both cross-sectional dependence and latent group structures, and estimating panel error-correction model with unobserved dynamic common factors.

Chapter 1 considers a panel cointegration model with latent group structures that allows for heterogeneous long-run relations across groups. We extend Su et al. (2013) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the dimension of the …

Three Essays On Large Panel Data Models With Cross-Sectional Dependence, Yonghui Zhang

Dissertations and Theses Collection (Open Access)

My dissertation consists of three essays which contribute new theoretical results to large panel data models with cross-sectional dependence. These essays try to answer or partially answer some prominent questions such as how to detect the presence of cross-sectional dependence and how to capture the latent structure of cross-sectional dependence and estimate parameters efficiently by removing its effects. Chapter 2 introduces a nonparametric test for cross-sectional contemporaneous dependence in large dimensional panel data models based on the squared distance between the pair-wise joint density and the product of the marginals. The test can be applied to either raw observable data …

Testing Heterogeneity In Panel Data Models With Interactive Fixed Effects, Qihui Chen

Dissertations and Theses Collection (Open Access)

This paper proposes a test for the slope homogeneity in large dimensional panel data models with interactive fixed effects based on a measure of goodness-of-fit (R²). We first obtain, for each cross-sectional unit, the R² from the time series regression of residuals on the constant and observable regressors and then construct the test statistic R² as an equally weighted average of the cross-sectional R²'s. R̄² is close to 0 under the null hypothesis of homogenous slopes and deviates away from 0 otherwise. We show that after being appropriately centered and scaled, R^{2 …}