Unlike the principles of traditional economics that substitution possibility holds for goods with monetary equivalency, mental accounting elaborates these goods would have different criteria values to consumers depending on purposes of its use and circumstances at purchase. Aided by modeling of inter-purchase duration that accommodates mental conditions changes captured by two latent variables, i.e. mental loading and household goods stock value, our research primarily intends to reveal how consumer's mental factor impacts consumer purchase behavior. Viewing from behavioral economics, the research attempts to comprehend consumer purchase behaviors that are seemingly irrational from traditional economics viewpoints. The model is derived from threshold-based modeling framework that incorporates consumer heterogeneity in a hierachical Bayesian manner, and modeling parameters are estimated using Markov Chain Monte Carlo (MCMC) mehod. Empirical studies have been exploited with ID-POS data of a retailer shop, and results indicate that our model outperforms by having consumer mental conditions changes into consideration at times of purchase.

This paper explores the effect of household real estate and financial asset ownership on household members' choice of employment. Analysis was conducted based on individual data from the Japanese National Survey of Family Income and Expenditure, which contains data on household members' employment, the composition of household incomes and the asset ownership of household members. Results suggest that the share of ``risky" assets including stocks and bonds among total household savings has a negative effect on head of households' employment, although the observed effect was limited. Results also show that real estate ownership has a negative effect on head of households' employment. These results indicate a theoretical possibility that the accumulation of household assets can motivate head of households to choose non-employment. The results also show a negative effect of residential land prices on head of households' employment.

In this paper, we introduce various multivariate stochastic volatility models and multivariate realized stochastic volatility models in the literature for the multiple asset returns, and discuss efficient Bayesian estimation methods using Markov chain Monte Carlo simulation.

Donald Rubin and Roderick Little have developed important basic theory of missing data analysis (e.g., Rubin (1976), Little and Rubin (1987, 2002)). They, however, have used useful but ambiguous notation Y_obs, Y_mis, so that it often causes misunderstanding. Using the precise notation, proper proofs are given for some statements on missing data analysis. Those include understanding comprehensively the method of maximum likelihood, giving a concise consistency proof of MLE, and clarifying relationship between MAR and conditional independence, role of the parameter distinction and efficacy of inclusion of auxiliary variables.

On estimating financial volatility models, Realized Stochastic Volatility (RSV) models use the information not only returns but also realized volatility measures, and it enables us to estimate the models efficiently.There are two popular approaches for estimating the RSV models; one is the Bayesian Markov chain Monte Carlo technique, and the other is the simulated maximum likelihood method based on the Monte Carlo likelihood.This paper examines a quasi-maximum likelihood method based on the Kalman filer.Especially, this paper compares efficiency of the estimator for the simple RSV model.As asymmetry, long memory, and heavy-tailed distributions are three important features for modeling volatility, the paper introduces various RSV models accommodating these structures, and gives details of the estimation methods based on the Kalman filter.The paper reports empirical results for the various RSV model using stock market indices of U.S., U.K., and Japan.