Essays in panel data analysis

This dissertation consists of two empirical studies on panel data models. In the first chapter, we study a pseudo panel data set with binary variables as outcome variables and variables of interest. We discuss the identification of a recursive bivariate probit model. We estimate the average treatment effects of health insurance and new treatment technology on the probability of being tested for HIV. In the second chapter, we study genius panel data and apply a factor analysis to Metropolitan Statistical Area (MSA)-level Housing Price Indexes (HPIs). With this, we estimate the number of common factors using Bai and Ng (2002)'s information criteria, and measure the closeness of the links between macroeconomic variables and the set of common factors.;The first chapter investigates the effects of health insurance and new antiviral treatments on HIV testing rates among the U.S. general population. A theoretical model is developed in which an agent decides whether or not to undergo HIV testing. This decision is determined by the value of early treatment and the value of identifying HIV-negative status. We test the predictions from the theoretical model by using nationally representative data from the Behavioral Risk Factor Surveillance Survey (BRFSS) for the years 1993 to 2002. We estimate a recursive bivariate probit model, with insurance coverage and HIV testing as the dependent variables. We use changes in Medicaid eligibility and distribution of firm size over time within a state as restriction exclusions for insurance coverage. Using a bootstrap method, we estimate robust confidence intervals of average treatment effects. Consistent with the theoretical model, the results suggest that (a) insurance coverage increases HIV testing rates, (b) insurance coverage increases HIV testing rates more among the high-risk population, and (c) the advent of Highly Active Antiretroviral Therapy (HAART) increases the effects of insurance coverage on HIV testing for high-risk populations.;The second chapter aims to identify common factors underlying fluctuations in the Metropolitan Statistical Area (MSA)-level Housing Price Indexes (HPIs). More importantly, we examine whether the observed macroeconomic variables are exact factors. For robustness, we study the two most popular housing price indexes: the Office of Federal Housing Enterprise Oversight (OFHEO) repeat sales index, and the Mortgage Risk Assessment Corporation (MRAC) median home price index. The methodology follows a two-step procedure: first, we look at several information criteria to determine the number of common factors that underlie fluctuations in the MSA-level HPIs. Next, we measure the overall closeness of the links between each macroeconomic variable and the set of common factors, using the factors estimated in the first step. Our results suggest that only a small number of factors capture the main co-movements of housing price time series data in the U.S.: Bai and Ng (2002)'s IC and PC criteria both suggest the presence of four factors, while Ahn and Horenstein (2009)'s criteria only finds one latent factor in the OFHEO panel. The first factor, called the "summary measure," closely tracks with the national index. The degree of closeness between this summary measure and the national index reflects the accuracy of weights assigned to census divisions in constructing the national index. We find a geographical pattern of factor loadings, which is useful in defining submarkets. Our comparison study shows that the MRAC HPIs are more volatile than the OFHEO HPIs. As a result, a larger number of factors are extracted. Finally, findings show that GDP, personal consumption, fixed private investment, employment growth, unemployment rates, Treasury bill market rates, and certificate of deposit market rates have strong correlations with the housing market. Financial markets have contemporary effects on the housing market while investment and consumption show lag effects. The measures in the second step reconfirm the presence of four latent factors in the housing price markets.