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Should We Demean the Data?

Author

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  • Yong Bao

    (Department of Economics, Purdue University)

Abstract

The sample average is an unbiased estimator of the population mean, so it may seem innocuous that for estimating model parameters that do not involve the population mean, the data can be demeaned first. Using a first-order moving average (MA) model for example, we derive the analytical approximate biases of the quasi maximum likelihood estimators (QMLEs) based on the original and demeaned data. The bias results indicate that the QMLEs can behave quite differently in finite samples and it is not always advisable to demean the data if the MA parameter is of primary interest to estimate.

Suggested Citation

  • Yong Bao, 2015. "Should We Demean the Data?," Annals of Economics and Finance, Society for AEF, vol. 16(1), pages 163-171, May.
    • Handle: RePEc:cuf:journl:y:2015:v:16:i:1:bao
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    References listed on IDEAS

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    1. Ullah, Aman, 2004. "Finite Sample Econometrics," OUP Catalogue, Oxford University Press, number 9780198774488.
    2. Bao, Yong & Ullah, Aman, 2007. "The second-order bias and mean squared error of estimators in time-series models," Journal of Econometrics, Elsevier, vol. 140(2), pages 650-669, October.
    3. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    4. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    5. Cordeiro, Gauss M. & Klein, Ruben, 1994. "Bias correction in ARMA models," Statistics & Probability Letters, Elsevier, vol. 19(3), pages 169-176, February.
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    Cited by:

    1. Funjika, Patricia & Getachew, Yoseph Y., 2022. "Colonial origin, ethnicity and intergenerational mobility in Africa," World Development, Elsevier, vol. 153(C).

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    More about this item

    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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