A1722
Title: GMM estimation of the long run risks model
Authors: Nour Meddahi - Toulouse School of Economics (France)
Jules Tinang - University of Groningen (Netherlands) [presenting]
Abstract: A GMM estimation of the structural parameters of the Long Run Risk model is proposed that allows for the separation between the consumer optimal decision 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, and the use of the estimated parameters to simulate the model enables us to improve some quantitative predictions of the model. We also show that the commonly used methods of statistical inference, such as the bootstrap (parametric or block bootstrap), might be misleading in this case since they imply an undercoverage of the true confidence interval.
