Estimating Household Consumption Insurance
22 Pages Posted: 16 Mar 2017 Last revised: 6 Nov 2020
Date Written: November 5, 2020
Blundell, Pistaferri, and Preston (American Economic Review, 2008, 98(5), 1887-1921) report an estimate of household consumption insurance with respect to permanent income shocks of 36%. In replicating findings for their model and data, we find that this estimate is distorted by a code error and is not robust to weighting scheme for GMM or consideration of quasi maximum likelihood estimation (QMLE), which produces a significantly higher estimate of consumption insurance at 55%. For sub-groups by age and education, the differences between estimates across methods are even more pronounced and QMLE provides new insights into heterogeneity across households compared to the original study. Monte Carlo experiments using non-Normal shocks suggest that consumption insurance estimates for the model are more accurate for QMLE than GMM, including when correcting for bias and especially given a smaller sample such as is only available when looking at sub-groups.
Keywords: consumption insurance; weighting schemes; quasi maximum likelihood
JEL Classification: E21; C13; C33
Suggested Citation: Suggested Citation