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Estimating Euler equations

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  • Orazio Attanasio

    ()
    (Institute for Fiscal Studies and University College London)

  • Hamish Low

    ()
    (Institute for Fiscal Studies and Trinity College, Cambridge)

Abstract

In this paper we consider conditions under which the estimation of a log-linearized Euler equation for consumption yields consistent estimates of preference parameters. When utility is isoelastic and a sample covering a long time period is available, consistent estimates are obtained from the log-linearized Euler equation when the innovations to the conditional variance of consumption growth are uncorrelated with the instruments typically used in estimation. We perform a Montecarlo experiment, consisting in solving and simulating a simple life cycle model under uncertainty, and show that in most situations, the estimates obtained from the log-linearized equation are not systematically biased. This is true even when we introduce heteroscedasticity in the process generating income. The only exception is when discount rates are very high (e.g. 47% per year). This problem arises because consumers are nearly always close to the maximum borrowing limit: the estimation bias is unrelated to the linearization and estimates using nonlinear GMM are as bad. Across all our situations, estimation using a log-linearized Euler equation does better than nonlinear GMM despite the absence of measurement error. Finally, we plot life cycle profiles for the variance of consumption growth, which, except when the discount factor is very high, is remarkably flat. This implies that claims that demographic variables in log- linearized Euler equations capture changes in the variance of consumption growth are unwarranted.

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Bibliographic Info

Paper provided by Institute for Fiscal Studies in its series IFS Working Papers with number W02/06.

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Length: 82 pp
Date of creation: Mar 2002
Date of revision:
Handle: RePEc:ifs:ifsewp:02/06

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  1. > Schools of Economic Thought, Epistemology of Economics > Economic Methodology > Dynamic Stochastic General Equilibrium > Solution Methods for DSGE models
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