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In this paper, we propose an adaptive group lasso procedure to efficientlyestimate structural breaks in cointegrating regressions. It is well-known thatthe group lasso estimator is not simultaneously estimation consistent and modelselection consistent in structural break settings. Hence, we use a first stepgroup lasso estimation of a diverging number of breakpoint candidates toproduce weights for a second adaptive group lasso estimation. We prove thatparameter changes are estimated consistently by group lasso and show that thenumber of estimated breaks is greater than the true number but stillsufficiently close to it. Then, we use these results and prove that theadaptive group lasso has oracle properties if weights are obtained from ourfirst step estimation. Simulation results show that the proposed estimatordelivers the expected results. An economic application to the long-run US moneydemand function demonstrates the practical importance of this methodology.

Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions