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
Date Written: November 1, 2008
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: Suggested Citation