Model Selection when there are Multiple Breaks
نویسندگان
چکیده
We consider model selection facing uncertainty over the choice of variables and the occurrence and timing of multiple location shifts. General-to-simple selection is extended by adding an impulse indicator for every observation to the set of candidate regressors: see Johansen and Nielsen (2009). We apply that approach to a fat-tailed distribution, and to processes with breaks: Monte Carlo experiments show its capability of detecting up to 20 shifts in 100 observations, while jointly selecting variables. An illustration to U.S. real interest rates compares impulse-indicator saturation with the procedure in Bai and Perron (1998). JEL classifications: C52, C22.
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