This paper investigates and extends the computationally attractive nonparametric random coefficients estimator of Fox, Kim, Ryan, and Bajari (2011). We show that their estimator is a special case of the nonnegative LASSO, explaining its sparse nature observed in many applications. Recognizing...

This paper investigates and extends the computationally attractive nonparametric random coefficients estimator of Fox, Kim, Ryan, and Bajari (2011). We show that their estimator is a special case of the nonnegative LASSO, explaining its sparse nature observed in many applications. Recognizing...

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...

In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component...

Motivated by the promising performance of alternative estimation methods for mixed logit models, in this paper we derive, implement, and test expectation-maximization (EM) and minorization-maximization (MM) algorithms to estimate the semiparametric logit mixed logit (LML) and mixture-of-normals...

Nonparametric regression is often considered as unreliable in small samples when the number of regressors is large. This may be different if the dependent variable is bounded. First, the small-sample properties of nonparametric regression with 14 regressors (4 continuous, 10 binary) are...

Individual players in a simultaneous equation binary choice model act differently in different environments in ways that are frequently not captured by observables and a simple additive random error. This paper proposes a random coefficient specification to capture this type of heterogeneity in...

This paper proposes a semiparametric estimator for spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects. The estimation procedure is based on the observational equivalence between distribution free models with a conditional median restriction and...

This contribution studies the application of heteroskedasticity robust estimation of Vector-Autoregressive (VAR) models. VAR models have become one of the most applied models for the analysis of multivariate time series. Econometric standard software usually provides parameter estimators that...

A non-stationary regression model for financial returns is examined theoretically. Volatility dynamics are modeled by nonparametric curve estimation on equidistant return vectors. We prove consistency and asymptotic normality of symmetric estimators and of one-sided estimators for variances and...