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Estimating Euler equations with noisy data: two exact GMM estimators
Author(s) -
Alan Sule,
Attanasio Orazio,
Browning Martin
Publication year - 2009
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1037
Subject(s) - estimator , mathematics , monte carlo method , econometrics , euler equations , computer science , statistics , mathematical analysis
In this paper we exploit the specific structure of the Euler equation and develop two alternative GMM estimators that deal explicitly with measurement error. The first estimator assumes that the measurement error is log‐normally distributed. The second estimator drops the distributional assumption at the cost of less precision. Our Monte Carlo results suggest that both proposed estimators perform much better than conventional alternatives based on the exact Euler equation or its log‐linear approximation, especially with short panels. An empirical application to the PSID yields plausible and precise estimates of the coefficient of relative risk aversion and the discount rate. Copyright © 2008 John Wiley & Sons, Ltd.