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Estimating Euler Equations with Noisy Data: Two Exact GMM Estimators

Author

Listed:
  • Sule Alan

    (Department of Economics, York University, Toronto)

  • Orazio Attanasio

    (Department of Economics, University College London)

  • Martin Browning

    (Institute of Economics, University of Copenhagen)

Abstract

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 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.

Suggested Citation

  • Sule Alan & Orazio Attanasio & Martin Browning, 2005. "Estimating Euler Equations with Noisy Data: Two Exact GMM Estimators," CAM Working Papers 2005-10, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    • Handle: RePEc:kud:kuieca:2005_10
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    nonlinear models; measurement error; Euler equation;
    All these keywords.

    JEL classification:

    • 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

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