In this paper we provide a Random-Utility based derivation of the Dirichlet-Multinomial regression and posit it as a convenient alternative for dealing with overdispersed multinomial data. We show that this model is a natural extension of McFadden's conditional logit for grouped data and show...

This paper shows how to cope with a problem of model selection and simplification using the principle of coherence (Gabriel (1969): A procedure involving testing a set of models ought not accept a model while rejecting a more general model). The mathematical lattice theory is used to define a...

Although semiparametric alternatives are available, parametric binary choice models are widely used in practice, in spite of their sensitivity to misspecification. Here we present the results of a simulation study on the finite sample performance of parametric and semiparametric specification...

The focus of this paper is an information theoretic-symbolic logic approach to extract information from complex economic systems and unlock its dynamic content. Permutation Entropy (PE) is used to capture the permutation patterns-ordinal relations among the individual values of a given time...

Nonlinear estimation of the gravity model with Poisson-type regression methods has become popular for modelling international trade flows, because it permits a better accounting for zero flows and extreme values in the distribution tail. Nevertheless, as trade flows are not independent from each...

In this paper, we develop a new model of a static game of incomplete information with a large number of players. The model has two key distinguishing features. First, the strategies are subject to threshold effects, and can be interpreted as dependent censored random variables. Second, in...

Allowing for misspecification in the linear conditional quantile function, this paper provides a new interpretation and the semiparametric efficiency bound for the quantile regression parameter b(t) in Koenker and Bassett (1978). The first result on interpretation shows that under a mean-squared...

This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS) estimation...

This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small) number of factors. I investigate the properties of the...