Markov-Chain Approximations for Life-Cycle Models

39 Pages Posted: 1 May 2019

See all articles by Giulio Fella

Giulio Fella

Queen Mary, University of London

Giovanni Gallipoli

Vancouver School of Economics, UBC; Centre for Economic Policy Research (CEPR); University of Chicago - Becker Friedman Institute for Economics; Rimini Centre for Economic Analysis

Jutong Pan

Analysis Group, Inc.

Date Written: December 18, 2018

Abstract

Non-stationary income processes are standard in quantitative life-cycle models, prompted by the observation that within-cohort income inequality increases with age. This paper generalizes Tauchen (1986), Adda-Cooper (2003), and Rouwenhorst’s (1995) discretization methods to non-stationary AR(1) processes. We evaluate the performance of these methods in the context of a canonical finite-horizon, income-fluctuation problem with a non-stationary income process. As a special case, we also examine relative performance when innovations to the persistent component of earnings are modeled as draws from a mixture of Normal distributions. We find that the generalized Rouwenhorst’s method performs consistently better than the others even with a relatively small number of states.

Keywords: numerical methods, finite state approximations

JEL Classification: C63

Suggested Citation

Fella, Giulio and Gallipoli, Giovanni and Pan, Jutong, Markov-Chain Approximations for Life-Cycle Models (December 18, 2018). Available at SSRN: https://ssrn.com/abstract=3364635 or http://dx.doi.org/10.2139/ssrn.3364635

Giulio Fella (Contact Author)

Queen Mary, University of London ( email )

Mile End Road
London E1 4NS, London E1 4NS
United Kingdom

Giovanni Gallipoli

Vancouver School of Economics, UBC ( email )

6000 Iona drive
Vancouver, BC BC V6T 1L4
Canada

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

University of Chicago - Becker Friedman Institute for Economics ( email )

Chicago, IL 60637
United States

Rimini Centre for Economic Analysis ( email )

Rimini
Italy

Jutong Pan

Analysis Group, Inc. ( email )

111 Huntington Avenue
10th floor
Boston, MA 02199
United States

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