Regression Monte Carlo for Impulse Control

I develop a numerical algorithm for stochastic impulse control in the spirit of Regression Monte Carlo optimal stopping. The approach consists generating statistical surrogates (aka functional approximators) continuation function. are recursively trained by empirical regression over simulated state trajectories. In parallel, same used to learn intervention function characterizing amounts. discuss appropriate surrogate types this task, as well choice training sets. Case studies from forest rotation and irreversible investment illustrate scheme highlight its flexibility extensibility. Implementation R is provided publicly available package posted on GitHub.