Abstract
A Bayesian approach is considered to detect the number of change points in linear regression model. The work is the extension of that given by Fan et al. (1996) for simple linear regression. The normal-gamma prior information for the regression parameters is employed in the analysis. The marginal posterior distribution of the location of change points and the number of change points are derived. Under mild assumptions, con-sistency for the number of change points and boundedness between the posterior mode of the location and true location of change points are also established. The Bayesian approach for the detection of the number of change points is suitable whether the switching linear regression is continuous or discontinuous. Some simulat-ed results are given.
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