Estimating Euler Equations with Noisy Data: Two Exact GMM Estimators
AbstractIn 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 lognormally distributed. The second estimator drops the distributional assumption and solves out for the unknown, but constant, conditional mean. 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.
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Bibliographic InfoPaper provided by University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics in its series CAM Working Papers with number 2005-10.
Length: 13 pages
Date of creation: May 2005
Date of revision:
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More information through EDIRC
nonlinear models; measurement error; Euler equation;
Other versions of this item:
- Sule Alan & Orazio Attanasio & Martin Browning, 2009. "Estimating Euler equations with noisy data: two exact GMM estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 309-324, 03.
- Martin Browning & Sule Alan, 2006. "Estimating Euler Equations with Noisy Data: Two Exact GMM Estimators," Economics Series Working Papers 283, University of Oxford, Department of Economics.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-07-03 (All new papers)
- NEP-DEV-2005-07-03 (Development)
- NEP-MAC-2005-07-03 (Macroeconomics)
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