IDEAS home Printed from https://ideas.repec.org/a/cuf/journl/y2015v16i1bao.html
   My bibliography  Save this article

Should We Demean the Data?

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://down.aefweb.net/AefArticles/aef160108Bao.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ullah, Aman, 2004. "Finite Sample Econometrics," OUP Catalogue, Oxford University Press, number 9780198774488, Decembrie.
    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. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    5. Cordeiro, Gauss M. & Klein, Ruben, 1994. "Bias correction in ARMA models," Statistics & Probability Letters, Elsevier, vol. 19(3), pages 169-176, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

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

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bao Yong & Zhang Ru, 2013. "Estimation Bias and Feasible Conditional Forecasts from the First-Order Moving Average Model," Journal of Time Series Econometrics, De Gruyter, vol. 6(1), pages 63-80, July.
    2. Stelios Arvanitis & Antonis Demos, 2015. "A class of indirect inference estimators: higher‐order asymptotics and approximate bias correction," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 200-241, June.
    3. Drobysheva, Alexandra (Дробышева, Александра) & Merzlyakov, Sergey (Мерзляков, Сергей), 2024. "The Forward Guidance Puzzle and Anchored Inflation Expectations," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, issue 6, pages 6-25.
    4. Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
    5. Bedri Kamil Onur Taş, 2016. "Does the Federal Reserve have Private Information about its Future Actions?," Economica, London School of Economics and Political Science, vol. 83(331), pages 498-517, July.
    6. Orphanides, Athanasios & Wei, Min, 2012. "Evolving macroeconomic perceptions and the term structure of interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 36(2), pages 239-254.
    7. Kitsul, Yuriy & Wright, Jonathan H., 2013. "The economics of options-implied inflation probability density functions," Journal of Financial Economics, Elsevier, vol. 110(3), pages 696-711.
    8. Verbrugge, Randal & Zaman, Saeed, 2023. "The hard road to a soft landing: Evidence from a (modestly) nonlinear structural model," Energy Economics, Elsevier, vol. 123(C).
    9. Stéphane Guibaud & Yves Nosbusch & Dimitri Vayanos, 2013. "Bond Market Clienteles, the Yield Curve, and the Optimal Maturity Structure of Government Debt," The Review of Financial Studies, Society for Financial Studies, vol. 26(8), pages 1914-1961.
    10. Ghrissi Mhamdi, 2013. "Stability Of Money Demand Function In Tunisia," Post-Print halshs-01138431, HAL.
    11. Hommes, Cars & Zhu, Mei, 2014. "Behavioral learning equilibria," Journal of Economic Theory, Elsevier, vol. 150(C), pages 778-814.
    12. Michele Lenza & Giorgio E. Primiceri, 2020. "How to Estimate a VAR after March 2020," NBER Working Papers 27771, National Bureau of Economic Research, Inc.
    13. Yu, Jun, 2012. "Bias in the estimation of the mean reversion parameter in continuous time models," Journal of Econometrics, Elsevier, vol. 169(1), pages 114-122.
    14. Dimitris Korobilis, 2021. "High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 493-504, March.
    15. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
    16. Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
    17. J. Scott Davis, 2012. "The effect of commodity price shocks on underlying inflation: the role of central bank credibility," Globalization Institute Working Papers 134, Federal Reserve Bank of Dallas.
    18. Muellbauer, John & Aron, Janine & Sebudde, Rachel, 2015. "Inflation forecasting models for Uganda: is mobile money relevant?," CEPR Discussion Papers 10739, C.E.P.R. Discussion Papers.
    19. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    20. Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.

    More about this item

    Keywords

    Demean; Moving Average; Bias;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cuf:journl:y:2015:v:16:i:1:bao. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Qiang Gao (email available below). General contact details of provider: https://edirc.repec.org/data/emcufcn.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.