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

Listed author(s):
  • Biørn, Erik

    ()

    (Dept. of Economics, University of Oslo)

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.

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File URL: http://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2014/memo-28-2014.pdf
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Paper provided by Oslo University, Department of Economics in its series Memorandum with number 28/2014.

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Length: 16 pages
Date of creation: 30 Dec 2014
Handle: RePEc:hhs:osloec:2014_028
Contact details of provider: Postal:
Department of Economics, University of Oslo, P.O Box 1095 Blindern, N-0317 Oslo, Norway

Phone: 22 85 51 27
Fax: 22 85 50 35
Web page: http://www.oekonomi.uio.no/indexe.html
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  1. Nowak, Eugen, 1993. "The identification of multivariate linear dynamic errors-in-variables models," Journal of Econometrics, Elsevier, vol. 59(3), pages 213-227, October.
  2. Grether, D M & Maddala, G S, 1973. "Errors in Variables and Serially Correlated Disturbances in Distributed Lag Models," Econometrica, Econometric Society, vol. 41(2), pages 255-262, March.
  3. Staudenmayer, John & Buonaccorsi, John P., 2005. "Measurement Error in Linear Autoregressive Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 841-852, September.
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