THURSDAY, February 28th, 2008
Speaker: Aaron Smith
(Agricultural and Resource Economics, UC Davis)
Title: Markov
Breaks in Regression Models
Abstract: I develop a new Markov breaks
(MB) model for forecasting and making inference in linear regression models
with stochastic breaks. The MB model permits an arbitrarily large number of
abrupt breaks in the regression coefficients and error variance, but it
maintains a low-dimensional state space, and therefore it is computationally
straightforward. The model generates forecasts and conditional parameter
estimates using a probability weighted average over regressions that include
progressively more historical data. I employ the MB model to study the
predictive ability of the yield curve for quarterly GDP growth. I show evidence
of breaks in the predictive relationship, and the MB model outperforms
competing breaks models in an out-of-sample forecasting experiment.
