Structural estimation of jump-diffusion processes in macroeconomics

Understanding the process of economic growth involves comparing competing theoretical models and evaluating their empirical relevance. Our approach is to take the neoclassical stochastic growth model directly to the data and make inferences about the model parameters of interest. In this paper, output follows a jump-diffusion process. By imposing parameter restrictions we derive two solutions in explicit form. Based on them, we obtain transition densities in closed form and employ maximum likelihood techniques to estimate the model parameters. In extensive Monte Carlo simulations we demonstrate that population parameters of the underlying data generating process can be recovered. We find empirical evidence for jumps in monthly and quarterly data on industrial production for the UK, the US, Germany, and the euro area (Euro12).