Serially Correlated Measurement Errors in Time Series Regression: The Potential of Instrumental Variable Estimators

Erik Biorn

No 28/2014, Memorandum from Oslo University, Department of Economics

Abstract: The measurement error problem in linear time series regression, with focus on the impact of error memory, modeled as finite-order MA processes, is considered. Three prototype models, two bivariate and one univariate ARMA, and ways of handling the problem by using instrumental variables (IVs) are discussed as examples. One has a bivariate regression equation that is static, although with dynamics, entering via the memory of its latent variables. The examples illustrate how 'structural dynamics' interacting with measurement error memory create bias in Ordinary Least Squares (OLS) and illustrate the potential of IV estimation procedures. Supplementary Monte Carlo simulations are provided for two of the example models.

Keywords: Errors in variables; ARMA; Error memory; Simultaneity bias; Attenuation; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C22 C26 C32 C36 C52 C53 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2014-12-30
New Economics Papers: this item is included in nep-ecm
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