IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v34y2007i4p471-486.html
   My bibliography  Save this article

Robustness of Inference for One-sample Problem with Correlated Observations

Author

Listed:
  • Perla Subbaiah
  • George Xia

Abstract

The inference about the population mean based on the standard t-test involves the assumption of normal population as well as independence of the observations. In this paper we examine the robustness of the inference in the presence of correlations among the observations. We consider the simplest correlation structure AR(1) and its impact on the t-test. A modification of the t-test suitable for this structure is suggested, and its effect on the inference is investigated using Monte Carlo simulation.

Suggested Citation

  • Perla Subbaiah & George Xia, 2007. "Robustness of Inference for One-sample Problem with Correlated Observations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(4), pages 471-486.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:471-486
    DOI: 10.1080/02664760701231906
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701231906
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664760701231906?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ansley, Craig F. & Newbold, Paul, 1980. "Finite sample properties of estimators for autoregressive moving average models," Journal of Econometrics, Elsevier, vol. 13(2), pages 159-183, June.
    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. Maul, D. & Schiereck, D., 2017. "The bond event study methodology since 1974," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 80723, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).

    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. André Klein & Guy Melard & Toufik Zahaf, 1998. "Computation of the exact information matrix of Gaussian dynamic regression time series models," ULB Institutional Repository 2013/13738, ULB -- Universite Libre de Bruxelles.
    2. Petros Dellaportas & Aleksandar Mijatovi'c, 2014. "Arbitrage-free prediction of the implied volatility smile," Papers 1407.5528, arXiv.org.
    3. Melard, Guy & Roy, Roch & Saidi, Abdessamad, 2006. "Exact maximum likelihood estimation of structured or unit root multivariate time series models," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 2958-2986, July.
    4. Mario G.R. Pagliacci & Constantin Anghelache & Alexandru Manole & Madalina Gabriela Anghel, 2016. "The Econometric Model for the Economic and Financial Analysis of Romanian International Trade," Romanian Statistical Review, Romanian Statistical Review, vol. 64(3), pages 53-66, September.
    5. Eric Ghysels & Lynda Khalaf & Cosmé Vodounou, 2003. "Simulation Based Inference In Moving Average Models," Annals of Economics and Statistics, GENES, issue 69, pages 85-99.
    6. Arslanturk, Yalcin & Balcilar, Mehmet & Ozdemir, Zeynel Abidin, 2011. "Time-varying linkages between tourism receipts and economic growth in a small open economy," Economic Modelling, Elsevier, vol. 28(1), pages 664-671.
    7. Krishnan, R. & Sen, Kunal, 1995. "Measuring persistence in industrial output: The Indian case," Journal of Development Economics, Elsevier, vol. 48(1), pages 25-41, October.
    8. Ai Deng & Pierre Perron, 2006. "A comparison of alternative asymptotic frameworks to analyse a structural change in a linear time trend," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 423-447, November.
    9. Michael A. Hauser, 1998. "Maximum Likelihood Estimators for ARMA and ARFIMA Models: A Monte Carlo Study," Econometrics 9809001, University Library of Munich, Germany.
    10. Yue Fang, 2005. "The Effect of the Estimation on Goodness‐of‐Fit Tests in Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 527-541, July.
    11. John Y. Campbell & N. Gregory Mankiw, 1987. "Are Output Fluctuations Transitory?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 102(4), pages 857-880.
    12. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
    13. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2008. "Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting," DES - Working Papers. Statistics and Econometrics. WS ws081406, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Emili Valdero Mora, 2002. "Linear least squares estimation of the first order moving average parameter," Working Papers in Economics 80, Universitat de Barcelona. Espai de Recerca en Economia.
    15. Vougas, Dimitrios V., 2008. "New exact ML estimation and inference for a Gaussian MA(1) process," Economics Letters, Elsevier, vol. 99(1), pages 172-176, April.
    16. Cribari-Neto, Francisco, 1993. "The Cyclical Component in Brazilian GDP," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 13(1), April.
    17. Stephen R. Blough, 1994. "Near common factors and confidence regions for present value models," Working Papers 94-3, Federal Reserve Bank of Boston.
    18. Cribari-Neto, Francisco, 1996. "On time series econometrics," The Quarterly Review of Economics and Finance, Elsevier, vol. 36(Supplemen), pages 37-60.
    19. Kim, Chang-Jin & Kim, Jaeho, 2013. "The `Pile-up Problem' in Trend-Cycle Decomposition of Real GDP: Classical and Bayesian Perspectives," MPRA Paper 51118, University Library of Munich, Germany.
    20. Holt, Matthew T. & Brandt, Jon A., 1985. "Forecasting Hog Prices Using Time Series Analysis Of Residuals," 1985 Annual Meeting, August 4-7, Ames, Iowa 278558, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

    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:taf:japsta:v:34:y:2007:i:4:p:471-486. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.