Political party preference is a crucial element in the analysis of economics and political science. However, it is often difficult to investigate the dynamic properties of the individual partisanship due to inaccessibility to panel data. This study proposes a Bayesian approach for estimating Markov dynamics of individual-level partisanship with repeated cross-section data in which the history of respondents' choice of favored party cannot be observed. The proposed approach identifies individual heterogeneities that affect transitional patterns of partisanship, and replicates the dynamic patterns of individual partisan mobility. Using the proposed method with American survey data, the study shows that age, education and race significantly influence partisan dynamics among Americans for three decades from 1972.

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In recent years, more and more consumers who cancelled their newspaper subscription have signed up for online-flyer portal sites. However, their site access logs are not necessarily linked with their purchase records stored in ID-POS data, which is a marketing problem from the sellers' point of view. As a solution to this problem, we propose a practical framework of a hidden Markov model which allows for the assessment of the flyer advertisement. We also present a hierarchical Bayesian model which sheds light on the information search and shopping behavior of consumers, given individual heterogeneity. Our results demonstrate that the subscribers of online flyers are more likely to visit stores as they have more flyer accesses, email advertisements from portal sites and the announcements of time-limited sales.