Random Coefficient Models for Multilevel Analysis

Random Coefficient Models for Multilevel Analysis

We propose a possible statistical model for both contextual analysis and slopes as outcomes analysis. These techniques have been used in multilevel analysis for quite some time, but a precise specification of the regression models has not been given before. We formalize them by proposing a random coefficient regression model, and we investigate its statistical properties in some detail. Various...

Bayesian Analysis of Random Coefficient AutoRegressive Models

Random Coefficient AutoRegressive (RCAR) models are obtained by introducing random coefficients to an AR or more generally ARMA model. These models have second order properties similar to that of ARCH and GARCH models. In this article, a Bayesian approach to estimate the first order RCAR models is considered. A couple of Bayesian testing criteria for the unit-root hypothesis are proposed: one i...

Random Coefficient Models ∗ Nicholas

Dynamic Random Coefficient Models A Note on Covariance Stationarity Conditions for Dynamic Random Coefficient Models

In this note we look at sufficient conditions for stationarity of a simple random coefficient model and find that this model is guaranteed to be stationary under strict conditions. JEL codes: C22

Robust Estimators for Random Coefficient Regression Models

Random coefficient regression models have received considerable attention, especially from econometricians. Previous work has assumed that the coefficients have normal distributions. The variances of the coefficients have, in previous papers, been estimated by maximum likelihood or by least squares methodology applied to the squared residuals from a preliminary (unweighted) fit. Maximum likelih...