Lack of signal error (LoSE) and implications for OLS regression: measurement error for macro data

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Lack of signal error (LoSE) and implications for OLS regression: measurement error for macro data

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Jeremy J. Nalewaik

Abstract

This paper proposes a simple generalization of the classical measurement error model, introducing new measurement errors that subtract signal from the true variable of interest, in addition to the usual classical measurement errors (CME) that add noise. The effect on OLS regression of these lack of signal errors (LoSE) is opposite the conventional wisdom about CME: while CME in the explanatory variables causes attenuation bias, LoSE in the dependent variable, not the explanatory variables, causes a similar bias under some conditions. In addition, LoSE in the dependent variable shrinks the variance of the regression residuals, making inference potentially misleading. The paper provides evidence that LoSE is an important source of error in US macroeconomic quantity data such as GDP growth, illustrates downward bias in regressions of GDP growth on asset prices, and provides recommendations for econometric practice.

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Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2008-15.

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Date of creation: 2008
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Handle: RePEc:fip:fedgfe:2008-15

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This paper has been announced in the following NEP Reports:

NEP-ALL-2008-04-12 (All new papers)
NEP-ECM-2008-04-12 (Econometrics)

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repec:fth:harver:1435 is not listed on IDEAS
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