A bayesian approach to time-varying cross-sectional regression models

Abstract

This paper presents a Bayesian approach to regression models with time-varying parameters, or state vector models. Unlike most previous research in this field the model allows for multiple observations for each time period. Bayesian estimators and their properties are developed for the general case where the regression parameters follow an ARMA(s,q) process over time. This methodology is applied to the estimation of time-varying price elasticity for a consumer product, using biweekly sales data for eleven domestic markets. The parameter estimates and forecasting performance of the model are compared with various alternative approaches.