Lack of Signal Error (LoSE) and Implications for OLS Regression: Measurement Error for Macro Data

49 Pages Posted: 15 Jan 2009 Last revised: 19 Jun 2017

See all articles by Jeremy Nalewaik

Jeremy Nalewaik

Board of Governors of the Federal Reserve System

Date Written: November 1, 2008

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. 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.

Keywords: Measurement error models, regression, macroeconomic data

JEL Classification: C1, E1, G1

Suggested Citation

Nalewaik, Jeremy John, Lack of Signal Error (LoSE) and Implications for OLS Regression: Measurement Error for Macro Data (November 1, 2008). FEDS Working Paper No. 2008-15. Available at SSRN: https://ssrn.com/abstract=1327129 or http://dx.doi.org/10.2139/ssrn.1327129

Jeremy John Nalewaik (Contact Author)

Board of Governors of the Federal Reserve System ( email )

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