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

Author

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  • Biørn, Erik

    (Dept. of Economics, University of Oslo)

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.

Suggested Citation

  • Biørn, Erik, 2014. "Serially Correlated Measurement Errors in Time Series Regression: The Potential of Instrumental Variable Estimators," Memorandum 28/2014, Oslo University, Department of Economics.
    • Handle: RePEc:hhs:osloec:2014_028
    as

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    File URL: http://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2014/memo-28-2014.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. Nowak, Eugen, 1993. "The identification of multivariate linear dynamic errors-in-variables models," Journal of Econometrics, Elsevier, vol. 59(3), pages 213-227, October.
    3. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Errors in variables; ARMA; Error memory; Simultaneity bias; Attenuation; Monte Carlo;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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