Estimating Household Consumption Insurance

22 Pages Posted: 16 Mar 2017 Last revised: 27 Aug 2019

See all articles by Arpita Chatterjee

Arpita Chatterjee

UNSW Australia Business School, School of Economics

James Morley

University of Sydney

Aarti Singh

The University of Sydney - School of Economics

Date Written: July 5, 2019

Abstract

Blundell, Pistaferri, and Preston (2008) report an estimate of household consumption insurance with respect to permanent income shocks of 36%. Their estimate is distorted by an error in their code and is not robust to weighting scheme for GMM. We propose instead to use quasi maximum likelihood estimation (QMLE), which produces a more precise and signi cantly higher estimate of
consumption insurance at 55%. For sub-groups by age and education, di erences between estimates are even more pronounced. Monte Carlo experiments with non-Normal shocks demonstrate that QMLE is more accurate than GMM, especially given a smaller sample size.

Keywords: consumption insurance; weighting schemes; quasi maximum likelihood

JEL Classification: E21; C13; C33

Suggested Citation

Chatterjee, Arpita and Morley, James and Singh, Aarti, Estimating Household Consumption Insurance (July 5, 2019). UNSW Business School Research Paper No. 2017-07. Available at SSRN: https://ssrn.com/abstract=2933226 or http://dx.doi.org/10.2139/ssrn.2933226

Arpita Chatterjee (Contact Author)

UNSW Australia Business School, School of Economics ( email )

High Street
Sydney, NSW 2052
Australia

James Morley

University of Sydney ( email )

Rm 370 Merewether (H04)
Sydney, NSW 2006 2008
Australia

HOME PAGE: http://https://sites.google.com/site/jamescmorley/

Aarti Singh

The University of Sydney - School of Economics ( email )

Rm 370 Merewether (H04)
Sydney, NSW 2006 2008
Australia

HOME PAGE: http://sydney.edu.au/arts/economics/staff/academic/aarti_singh.shtml

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