"This dissertation consists of three chapters concerning both empirical studies and estimation mythologies of the discrete choice models in the area of demand estimation. The first chapter is a pure empirical study of estimating Chinese outbound tourism demand under a discrete choice model framework. The second chapter considers a mixture discrete choice model in which consumers have unobservable and heterogeneous choice sets and proposes a corresponding two-step mixture estimation approach. The third chapter contains a set of simulation studies regarding the two-step mixture approach proposed in the second chapter. More specifically, the first chapter implements a discrete choice approach to estimate the determinants of Chinese outbound tourism demand after year 2004, since when Chinese citizens could travel to most major overseas destinations without political restrictions. Starting from travelers' utility specifications, this chapter implements basic linear regressions to estimate Chinese tourists' sensitivity to the cost of travel and other characteristics of the destinations. The price and income elasticities are estimated as well. This chapter also proposes a strategy to quantify the welfare gains of Chinese tourists from the opening of Taiwan (to mainland China) as a new destination. The second chapter proposes a two-step mixture approach to estimate discrete choice models when consumers' choice sets are unobservable and heterogeneous. Different choice sets are viewed as different consumer types. Each type of consumers has distinct criteria on the attributes of products according to which their choice sets are formatted. After assuming the choice set formation process, the choice sets distribution and preference parameters can be jointly estimated by a two-step mixture approach. A key insight is that the approach can be applied to store level data. While having individual level data is not a must, it can provide guidance on the formation of choice sets. The effectiveness of the proposed mixture approach is demonstrated via a set of Monte Carlo simulations and three empirical applications on markets of milk, potato chips and hot-dogs using the IRI marketing data. The third chapter is a follow-up of Chapter 2 and is based on more simulation studies. In this chapter I review the data generation process (DGP) of my mixture model, discuss the failure of another estimation method which depends on the BLP-type inversion under my DGP setup, and then conduct Monte Carlo simulation experiments to examine the validity of the two-step mixture approach and demonstrate its superiority over other traditional estimation methods under various scenarios."--Pages ii-iii.
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