GMM estimation of the Long Run Risks model

In this paper, we propose a Gmm estimation of the structural parameters of the Long Run Risk model that allows for the separation between the consumer optimal decision's frequency and the frequency by which the econometrician observes the data. Our inference procedure is also robust to weak identification. The key finding is that the Long Run Risk model adapts well to the data but could not be so good at forecasting or telling the true story about what drives the evolution of asset prices. Indeed, the model is able to reproduce the qualitative behavior of targeted moments in the long run when the corresponding estimates of the structural parameters are used for simulations, but it also faces a urge tension in keeping in track with all the observed moments considered.