Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions
This work addresses a specific econometric challenge for researchers analyzing time series with structural changes, offering an incremental improvement over existing group lasso methods.
The paper tackles the problem of estimating structural breaks in cointegrating regressions by proposing an adaptive group lasso procedure, which achieves oracle properties and is validated through simulations and an economic application to US money demand.
In this paper, we propose an adaptive group lasso procedure to efficiently estimate structural breaks in cointegrating regressions. It is well-known that the group lasso estimator is not simultaneously estimation consistent and model selection consistent in structural break settings. Hence, we use a first step group lasso estimation of a diverging number of breakpoint candidates to produce weights for a second adaptive group lasso estimation. We prove that parameter changes are estimated consistently by group lasso and show that the number of estimated breaks is greater than the true number but still sufficiently close to it. Then, we use these results and prove that the adaptive group lasso has oracle properties if weights are obtained from our first step estimation. Simulation results show that the proposed estimator delivers the expected results. An economic application to the long-run US money demand function demonstrates the practical importance of this methodology.