Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Consider inference about the pre and post break value of a scalar parameter in a time series model with a single break at an unknown date. Unless the break is large, treating the break date estimated by least squares as the true break date leads to substantially oversized tests and confidence intervals. To develop a suitable alternative, we first establish convergence to a Gaussian process limit experiment. We then determine a nearly weighted average power maximizing test in this limit experiment, and show how to implement a small sample analogue in GMM time series models.