Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable...

We present the censored regression model with the error term following the asymmetric exponential power distribution. We propose three Markov chain Monte Carlo (MCMC) algorithms: the first one uses the probability integral transformation; the second one uses a combination of the probability...

We simplify the implementation of some elliptical copula regression models through the normal representation. Both copula and marginal probability density functions are expressed as the scale mixtures of normals to facilitate the estimation procedure. With the fact that all elliptical...

We consider two problems concerning locating change points in a linear regression model. One involves jump discontinuities (change-point) in a regression model and the other involves regression lines connected at unknown points. We compare four methods for estimating single or multiple change...

For many low-income countries, the impact of structural reforms on economic growth and poverty alleviation crucially depends on the response of aggregate agricultural supply to changing incentives. Despite its policy relevance, the size of this parameter is still largely unknown. This paper...

Chapter 1. Introductory Developments -- Chapter 2. The Simple Regression Model -- Chapter 3. The Multiple Regression Model -- Chapter 4. Heteroskedasticity and Autocorrelation of Errors -- Chapter 5. Problems With Explanatory Variables -- Chapter 6. Distributed Lag Models -- Chapter 7. An...

The paper considers the task of selecting a flexible nonlinear model which can be used as a baseline model. The baseline model may be used as a testing ground for more structural models which are congruent with economic theory. From the limited empirical evidence obtained here it is tentatively...

This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function...