TESTING THE LONG-RUN RISK MODEL: A KALMAN FILTER APPROACH

Abstract

This paper reevaluates the Long-Run Risk model proposed by Bansal and Yaron (2004) using the Kalman filter and Maximum Likelihood estimation method. Our findings show that the persistence of the small long-run predictable component in the consumption growth process is the key for the model performance. In our estimation exercises, if we relax the persistence restriction on the long-run risk parameter and adopt a Maximum Likelihood estimate, the Long-Run Risk model still requires a relative risk aversion at around 70 to fit the US data. However, we do not find strong empirical support for the persistence restriction from the data.