Measurement Error in a First-order Autoregression

Philip Hans Franses

Advances in Decision Sciences, 2020, vol. 24, issue 2, 1-14

Abstract: The Ordinary Least Squares (OLS) estimator for the slope parameter in a first-order autoregressive model is biased when the variable is measured with error. Such an error may occur with revisions of macroeconomic data. This paper illustrates and proposes a simple procedure to alleviate the bias, and is based on Total Least Squares (TLS). TLS is, in general, consistent, and also works well in small samples. Simulation experiments and an empirical example show the usefulness of this method.

Keywords: Errors-in-variables; OLS; First-order autoregression; Total Least Squares (search for similar items in EconPapers)
JEL-codes: C20 C51 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:aag:wpaper:v:24:y:2020:i:2:p:1-14

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