Finite-State Markov-Chain Approximations: A Hidden Markov Approach
This paper proposes a novel finite-state Markov chain approximation method for Markov processes with continuous support. The method can be used for both uni- and multivariate processes, as well as non-stationary processes such as those with a life-cycle component.The method is based on minimizing the information loss between a misspecified approximating model (a Hidden Markov Model) and the true data-generating process. We prove that and find conditions under which this information loss can be made arbitrarily small if enough grid points are used. In contrast to existing methods, the method provides both an optimal grid and transition probability matrix. The method outperforms existing methods in several dimensions, including parsimoniousness. We compare the performance of our method to existing methods through the lens of an asset-pricing model, and a life-cycle consumption-savings model. We find the choice of the discretization method matters for the accuracy of the model solutions, the welfare costs of risk, and the amount of wealth inequality a life-cycle model can generate
Date: 25 April 2023, 13:00 (Tuesday, 1st week, Trinity 2023)
Venue: Manor Road Building, Manor Road OX1 3UQ
Venue Details: Seminar Room D, Manor Road Building
Speaker: Eva Janssens (Federal Reserve Board)
Organising department: Department of Economics
Part of: Nuffield Econometrics Seminar
Booking required?: Not required
Audience: Members of the University only
Editor: Daria Ihnatenko