UC Irvine

This dissertation features a selection of Bayesian estimation frameworks for a variety of data and modeling scenarios. Economists seldom work with data arising from a prototypical linear regression model. In reality, data may be discrete, mixed, or missing; the true model may be semiparametric or nonparametric; effects may be nonadditive, or it may be heterogeneous across sampling units; observational data may be subject to sample selection and endogeneity issues; and the data might be coming from a sequential process. We discuss estimation methodologies for multifaceted data arising from complex processes. Special emphasis is placed on computational and mixing efficiency.

The first chapter develops a class of Markov process smoothness priors for potentially nonadditive multivariate mean functions. The proposed class of priors is incorporated into a larger semiparametric estimation framework that allows for endogeneity and sample selection, and is used to study how age and population density jointly affect car use in Japan. We find that an increase in density leads to a reduction in car use, and that this effect is smaller for elderly drivers than for young drivers. We also uncover interesting nonlinearities in the relationship between age and car use. Our empirical findings highlight the importance of allowing for nonlinear and nonadditive effects in our models.

The second chapter develops an econometric framework for estimating the effect of the built environment on transportation mode choice and usage when a large fraction of the population under study is nonlicensed. We use a multivariate ordinal outcomes model with a binary selection component to allow for heterogeneity in built environment effects and indirect effects urban form has on mode usage via license choice. Our exposition focuses on the joint modeling of correlated and discrete outcomes (binary and ordinal), strategizing with identification restrictions and nonidentification, and the efficient estimation of model parameters. A separate contribution this paper makes to the urban/transportation economics literature is the cross entropy index for land use imbalance, which we propose as a replacement to the entropy index for land use mix/balance. Using data from the 5th Nationwide Person Trip Survey (NPTS), we investigate whether the built environment is a policy-relevant determinant of travel behavior in the Japanese elderly. Effects are found to be nonzero but modest at best. Our results and conclusions are broadly consistent with those based on the United States.

In the third chapter, we design a Bayesian estimation method for the binary logit model with fixed and random effects. To this end, we develop a Gibbs procedure that uses sparse matrix algorithms, vectorization (parallelization), and collapsing to achieve computational and mixing efficiency. The method is fully Gibbs in that it does not involve a Metropolis-Hastings step anywhere. We also discuss the computation of the marginal likelihood, which is used to compare and select models. A detailed simulation study shows that the proposed methodology works well.