Doctor of Philosophy, The Ohio State University, 2016, Economics

Spatial econometrics has been obtained more and more attention in the recent years. The spatial autoregressive (SAR) model is one of the most widely used and studied models in spatial econometrics. So far, most studies have been focused on linear SAR models. However, some types of spatial or network data, for example, censored data or discrete choice data, are very common and useful, but not suitable to study by a linear SAR model. That is why I study an SAR Tobit model and an SAR binary choice model in this dissertation. Chapter 1 studies a Tobit model with spatial autoregressive interactions. We consider the maximum likelihood estimation (MLE) for this model and analyze asymptotic properties of the estimator based on the spatial near-epoch dependence (NED) of the dependent variable process generated from the model structure. We show that the MLE is consistent and asymptotically normally distributed. Monte Carlo experiments are performed to verify finite sample properties of the estimator. Chapter 2 extends the MLE estimation of the SAR Tobit model studied in Chapter 1 to distribution-free estimation. We examine the sieve MLE of the model, where the disturbances are i.i.d. with an unknown distribution. This model can be applied to spatial econometrics and social networks when data are censored. We show that related variables are spatial NED. An important contribution of this chapter is that I develop some exponential inequalities for spatial NED random fields, which are also useful in other semiparametric studies when spatial correlation exists. With these inequalities, we establish the consistency of the estimator. Asymptotic distributions of structural parameters of the model are derived from a functional central limit theorem and projection. Simulations show that the sieve MLE can improve the finite sample performance upon misspecified normal MLEs, in terms of reduction in the bias and standard deviation. As an empirical application, we examine the school (open full item for complete abstract)

Committee: Lung-fei Lee (Advisor); Jason Blevins (Committee Member); Robert de Jong (Committee Member) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 2007, Economics

My dissertation research emphasizes estimation methods in evaluating the extent of social, strategic and spatial interactions among economic agents. My first essay, based on my joint research with Lung-fei Lee and John Kagel, generalizes Heckman's (1981) dynamic discrete-choice panel data models by introducing time-lagged social interactions and proposes simulation based methods to implement the maximum likelihood estimation. We use this generalized model to investigate learning from peers in experimental signaling games. We find that subjects' decisions are significantly influenced by the past decisions of their peers in the experiment. Hence the imitation of peers' strategies is an important component of the learning process of strategic play. My second essay explores the robustness of Guerre, Perrigne and Vuong's (2000) two-step nonparametric estimation procedure in first-price sealed-bid auctions with a large number of risk-averse bidders. With an asymptotic approximation of the intractable equilibrium bidding function of risk-averse bidders, I demonstrate that Guerre et al.'s two-step nonparametric estimator based on the equilibrium bidding behavior of risk-neutral bidders is still uniformly consistent even if bidders are risk-averse as long as the number of players in an auction is sufficiently large and derive the uniform convergence rate of the estimator. Furthermore, I show in Monte Carlo experiments that the two-step nonparametric estimator performs reasonably well with a moderate number of risk-averse bidders like six. In my third essay, which is based on my joint research with Lung-fei Lee and Christopher Bollinger, we consider the GMM estimation of the regression model with spatial autoregressive disturbances and the mixed-regressive spatial autoregressive model. We derive the best GMM estimator within the class of GMM estimators that are based on linear and quadratic moment conditions. Our best GMM estimator has the merit of computational simplicity an (open full item for complete abstract)

Committee: Lung-fei Lee (Advisor) Subjects: Economics, General

Doctor of Philosophy, The Ohio State University, 2005, Economics

ABSTRACT This study investigates the lifecycle of social capital formation at the individual level. A dynamic model is developed that analyzes individuals' decision making about social capital accumulation that incorporates characteristics specific to social capital. The structural parameters of the model are estimated that address a variety of social capital issues. Theoretical Model The notion that people build up a network of friends (stock of social capital) by spending time in interacting with others (investment in social capital) is conducive to a neoclassical treatment. The model proposes a two-part return specification where, as distinct from the usual lagged return from stocks, social capital has an instantaneous return in the form of a direct utility accrued from the investment activity itself. The model allows for both the opportunity cost of time and depreciation rates to vary over the lifecycle. When parameterized the model can generate a variety of time paths of interest and allows for comparative dynamic exercises by perturbing parameter values. Econometric Model The structural parameters of the model are estimated using the method of simulated moments where matching is done using a GMM-type minimum distance estimation procedure. The data set used is from the General Social Survey (1972-2002). Chi-square statistics are calculated to test various restrictions to determine whether the parameter estimates are different among different groups. Results and Findings This study finds that social capital does depreciate and this depreciation rate varies over the lifecycle. The stylized fact of existing research that the time path of the stock of social capital has an inverted U-shape is not supported. Net benefits are higher for people with more education and which leads them to invest more in social capital despite having a higher opportunity costs of investment. This resolves a paradox that previous research could not explain. When comparative inves (open full item for complete abstract)

Committee: Donald Haurin (Advisor) Subjects: