Three essays on panel and factor models

• Main Author: FENG, Ji
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Cross Sectional Dependence; Heterogeneity Bias Test; Large panels; Random Coefficients; Specification Tests; Cryptocurrency price; Explosive behavior; Factor model; Idiosyncratic bubbles; Mildly explosive processes; Local-to-unity Explosive process; Common bubbles; Behavioral Economics; Economics
• Online Access: https://ink.library.smu.edu.sg/etd_coll/354; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1352&context=etd_coll
• Institution: Singapore Management University

The dissertation includes three chapters on panel and factor models. In the first chapter, we introduce a two-way linear random coefficient panel data models with fixed effects and the cross-sectional dependence. We follow the idea of the within-group fixed effects estimator to estimate parameters of interests. We establish the limiting distributions of the estimates and also propose the two-way heterogeneity bias test to check the desirability of the estimation strategy. The specification tests then are constructed to examine the existence of the slope heterogeneity and time-varyingness. We study the asymptotic properties of the specification tests and employ two bootstrap schemes to rectify the downward size distortion of the specification tests. We apply the specification tests to reveal the heterogenous relationship between the unemployment rate and youth labor rate in the working-age population. In the second chapter, we devise a simple but effective procedure to test bubbles in the idiosyncratic components in the presence of nonstationary or mildly explosive factors in common components in panel factor models. We study the asymptotic properties of our test. We also propose a wild bootstrap procedure to improve the finite sample performance of our test. As an illustrative example, we consider testing the bubbles in the idiosyncratic components of cryptocurrency prices. In the third chapter, we propose the tests constructed from estimated common factors for detecting bubbles in unobserved common factors when the idiosyncratic components follow a unit-root or local-to-unity process. We study the asymptotic properties of our proposed tests. We show that our proposed tests have non-trivial power to detect those bubbles in unobserved common factors under the alternative of local-to-unity. To implement our proposed tests, we propose to use the dependent wild bootstrap method to simulate the critical values in practice.