This paper has two major objectives. First, we develop and implement a Bayesian generalized factor model that allows for non-orthogonality of the idiosyncratic factors and the flexibility of cross-sectional and time series dimensions. Second, we evaluate the significance of the orthogonality...

Antedependence (AD) of order p, also known as the Markov property of order p, is a property of index-ordered random variables in which each variable, given at least p immediately preceding variables, is independent of all further preceding variables. Zimmerman and Nunez-Anton (2010) present...

This working paper refers to the evaluation of health states when equity matters. We propose an evaluation formula that incorporates equity concerns from an equality of opportunity viewpoint and is applicable to categorical data, such as self-reported qualitative health states. An empirical...

A significant aspect of data modeling with categorical predictors is the definition of a saturated model. In fact, there are different ways of specifying it—the casewise, the contingency table, and the collapsing approaches—and they strictly depend on the unit of analysis considered. The...

We study the problem of ergodicity, stationarity and maximum likelihood estimation for multinomial logistic models that include a latent process. Our work includes various models that have been proposed for the analysis of binary and, more general, categorical time series. We give verifiable...

Models with random effects/latent variables are widely used for capturing unobserved heterogeneity in multilevel/hierarchical data and account for associations in multivariate data. The estimation of those models becomes cumbersome as the number of latent variables increases due to...

The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and...