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Submitted by pscully on Thu, 05/08/2008 - 15:42.
02/28/2008 - 16:10
02/28/2008 - 17:30
STA/BST 290: Aaron Smith
Markov Breaks in Regression Models
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.