| 讲座简介： | This paper aims at capturing time-varying spillover effects in a panel data setting. We consider panel models where the outcome of a unit not only depends on its own characteristics and also the characteristics of other units (spillover effects). The effect of own characteristics can be unit-specific or homogeneous (common effects). We allow the linkage structure, i.e., which units interact with which, to be latent. Moreover, the structure and the spillover effects may both change at an unknown break point. To estimate the break point, linkage structure, and spillover and common effects, we solve a penalized least squares optimization and employ double machine learning procedures to improve the convergence and inference. We establish the super consistency of the break point estimator, which allows us to make inferences on other parameters as if the break point was known. We illustrate the theory via simulated and empirical data. |
