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Title: Detecting at-most-m changes in linear regression model Authors:  William Pouliot - University of Birmingham (United Kingdom) [presenting]
Lajos Horvath - University of Utah (USA)
Shixuan Wang - Cardiff Univeristy (United Kingdom)
Abstract: A new procedure is provided to test for at most two changes in the time dependent regression model $y_t = x_t \bm{\beta}_t + e_t$ with $1\le t \le T$. Our procedure is based on weighted sums of the residuals, incorporating the possibility of two changes. The weak limit of the proposed test statistics the sum of two double exponential random variables. A small Monte Carlo simulation illustrates the applicability of the limit results in case of small and moderate sample sizes. We compare the new method to the CUSUM and standardized (weighted) CUSUM procedures and obtain the power curves of the test statistics under the alternative. We apply the method to find changes in the unconditional four factor CAPM.