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Title: Structural breaks in seemingly unrelated regression models Authors:  Shahnaz Parsaeian - University of Kansas (United States) [presenting]
Abstract: An efficient Stein-like shrinkage estimator is developed for estimating the slope parameters under structural breaks in seemingly unrelated regression models, which then is used for forecasting. The proposed method is a weighted average of two estimators: a restricted estimator which estimates the parameters under the restriction of no break in the coefficients, and an unrestricted estimator which considers breakpoints and estimates the parameters using the observations within each regime. It is established that the asymptotic risk of the Stein-like shrinkage estimator is smaller than that of the unrestricted estimator which is the common method for estimating the slope coefficients under structural breaks. Furthermore, an averaging minimal mean squared error estimator is proposed where the averaging weight is derived by minimizing its asymptotic risk. The superiority of the two proposed estimators over the unrestricted estimator in terms of the mean squared forecast errors are derived. Besides, an analytical comparison between the asymptotic risks of the proposed estimators is provided. Insights from the theoretical analysis are demonstrated in Monte Carlo simulations, and on two empirical examples of forecasting U.S. industry-level inflation rates, and forecasting output growth rates of G7 countries.