Heterogeneous effects are prevalent in many economic settings. As the functional form between outcomes and regressors is generally unknown a priori, a semiparametric negative binomial count data model is proposed which is based on the local likelihood approach and generalized product kernels....

There has been continuing interest in Bayesian regressions without imposing any parametric assumption on the error distribution, but the asymptotic efficiency of such procedures has not been fully understood yet. In this article, we consider semiparametric Bayesian nonlinear regression models....

An important and widely used class of semiparametric models is formed by the varying-coefficient models. Although the varying coefficients are traditionally assumed to be smooth functions, the varying-coefficient model is considered here with the coefficient functions containing a finite set of...

Sorting and spillovers can create correlation in individual outcomes. In this situation, standard discrete choice estimators cannot consistently estimate the probability of joint and conditional events, and alternative estimators can yield incoherent statistical models or intractable estimators....

Traditional measures of dependence in time series are typically based on correlations or periodograms. These are adequate in many circumstances but, in others, especially when trying to assess market linkages (e.g., financial contagion), might be inappropriate. In the present paper we propose...

The current study investigated a cointegration and nonlinear causality relationships between inflation and repo rates of South Africa using the data spanning the period of January 2002 to March 2016. We used a threshold vector error correction model (TVECM) and nonlinear Granger frameworks...

This paper provides an approach to estimation and inference for non-linear conditional mean panel data models, in the presence of cross-sectional dependence. We modify the common correlated effects (CCE) correction of Pesaran (2006) to filter out the interactive unobserved multifactor structure....

This paper investigates the underlying causes of suicide. In contrast to previous literature, we use data from the United States at the county level. Our primary methodology is a two-level Bayesian hierarchical model with spatially correlated random effects. Our results show that the significant...