Using machine learning methods in a difference-in-differences setting, we quantify the population of informal workers generated by an irregular migration shock. We exploit the exogenous variation from the Arab Spring wave on southern Italian coasts, and a rich data on Italian vineyards. We...

We study inference for threshold regression in the context of a large panel factor model with common stochastic trends. We develop a Least Squares estimator for the threshold level, deriving almost sure rates of convergence and proposing a novel, testing based, way of constructing confidence...

This paper studies the asymptotic validity of sieve bootstrap for nonstationary panel factor series. Two main results are shown. Firstly, a bootstrap Invariance Principle is derived pointwise in i, obtaining an upper bound for the order of truncation of the AR polynomial that depends on n and T....