John C. Liechty

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Clickstream data provides information about the sequence of pages or the path viewed by users as they navigate a web site. We show how path information can be categorized and modeled using a dynamic multinomial probit model of web browsing. We estimate this model using data from a major online bookseller. Our results show that the memory component of the(More)
Because most conjoint studies are conducted in hypothetical situations with no consumption consequences for the participants, the extent to which the studies are able to uncover “true” consumer preference structures is questionable. Experimental economics literature, with its emphasis on incentive alignment and hypothetical bias, suggests that more(More)
We propose a method for optimal portfolio selection using a Bayesian framework that addresses two major shortcomings of the Markowitz approach: the ability to handle higher moments and estimation error. We employ the skew normal distribution which has many attractive features for modeling multivariate returns. Our results suggest that it is important to(More)
We develop a statistical model of browsing behavior by predicting the number of web pages, in a particular category, that are viewed by a user in a single web session. The purpose of this analysis is to better understand web browsing behavior, and to help predict which sessions are likely to result in retail visits. A single record in our database consists(More)
We propose prior probability models for variance-covariance matrices in order to address two important issues. First, the models allow a researcher to represent substantive prior information about the strength of correlations among a set of variables. Secondly, even in the absence of such information, the increased flexibility of the models mitigates(More)
We identify gaps and propose several directions for future research in preference measurement. We structure our argument around a framework that views preference measurement as comprising three inter-related components: 1) the problem that the study is ultimately intended to address; 2) the design of the preference measurement task and the data collection(More)
In this paper, we advocate incorporating the economic objective function into parameter estimation by analyzing the optimal portfolio choice problem of a mean-variance investor facing parameter uncertainty. We show that, in estimating the optimal portfolio weights, the standard plug-in approach that replaces the population parameters by their sample(More)
Markov chain Monte Carlo (MCMC) algorithms offer a very general approach for sampling from arbitrary distributions. However, designing and tuning MCMC algorithms for each new distribution, can be challenging and time consuming. It is particularly difficult to create an efficient sampler when there is strong dependence among the variables in a multivariate(More)
Eye movements across advertisements express a temporal pattern of bursts of respectively relatively short and long saccades, and this pattern is systematically influenced by activated scene perception goals. This was revealed by a continuous-time hidden Markov model applied to eye movements of 220 participants exposed to 17 ads under a free-viewing(More)