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EULER EQUATION ESTIMATION ON MICRO DATA

Published online by Cambridge University Press:  25 June 2018

Sule Alan ,
Kadir Atalay  and
Thomas F. Crossley
Sule Alan
Affiliation:
University of Essex
Kadir Atalay*
Affiliation:
University of Sydney
Thomas F. Crossley
Affiliation:
University of Essex and Institute for Fiscal Studies
*
Address correspondence to: Kadir Atalay, School of Economics, University of Sydney, Sydney, NSW 2006, Australia; e-mail: [email protected]
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Abstract

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Consumption Euler equations are important tools in empirical macroeconomics. When estimated on micro data, they are typically linearized, so standard IV or GMM methods can be employed to deal with the measurement error that is endemic to survey data. However, linearization, in turn, may induce serious approximation bias. We numerically solve and simulate six different life-cycle models, and then use the simulated data as the basis for a series of Monte Carlo experiments in which we evaluate the performance of linearized Euler equation estimation. We sample from the simulated data in ways that mimic realistic data structures. The linearized Euler equation leads to biased estimates of the EIS, but that bias is modest when there is a sufficient time dimension to the data, and sufficient variation in interest rates. However, a sufficient time dimension can only realistically be achieved with a synthetic cohort. Estimates from synthetic cohorts of sufficient length, while often exhibiting small mean bias, are quite imprecise. We also show that in all data structures, estimates are less precise in impatient models.

Keywords

Euler EquationsMeasurement ErrorInstrumental VariablesGMM
Type
Articles
Information
Macroeconomic Dynamics , Volume 23 , Issue 8 , December 2019 , pp. 3267 - 3292
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2018

Footnotes

K. Atalay acknowledges the support of the Australian Research Council (Grant #DP150101718). T. F. Crossley acknowledges support from the ESRC through the ESRC-funded Centre for Microeconomic Analysis of Public Policy at the Institute for Fiscal Studies (reference RES-544-28-5001) and through the Research Centre on Micro-Social Change (MiSoC) at the University of Essex, (reference ES/L009153/1). We also thank seminar participants at the University of Cambridge, University of Sydney and participants in the Journal of Applied Econometrics Workshop, and especially Anastasia Burkovskaya, Garry Barrett, Hamish Low, Simon Kwok, and Hashem Pesaran for helpful comments. All errors are our own.

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