IDEAS home Printed from https://ideas.repec.org/p/vic/vicewp/9802.html
   My bibliography  Save this paper

Testing for Unit Roots With Missing Observations

Author

Abstract

This paper considers unit root testing of time-series data with missing observations. Three procedures for dealing with the gaps are discussed. These include: ignoring the gaps, replacing the gaps with the last available observation, and filling the gaps with a linear interpolation method. The tests for the first two procedures yield test statistics which have the same asymptotic distribution as that tabulated by Dickey and Fuller (1979) for the complete data situation. The remaining procedure yields a test statistic that has an asymptotic distribution that differs from Dickey and Fuller’s tabulated distribution by an adjustment factor. In addition, models that include an ARIMA (0,1,q) error and augmented Dickey-Fuller tests are also considered in this paper. A simulation experiment is performed for the above models using the A-B sampling scheme. The results show that ignoring gaps in time- series data with missing observations produces unit root tests that are more powerful than the other two approaches that are considered.

Suggested Citation

  • Kevin F. Ryan & David E. A. Giles, 1998. "Testing for Unit Roots With Missing Observations," Econometrics Working Papers 9802, Department of Economics, University of Victoria.
    • Handle: RePEc:vic:vicewp:9802
    Note: ISSN 1485-6441. This paper was presented at the CEFES98 Meetings, Cambridge, U.K., June 1998, & at the 3rd Meeting of the New Zealand Econometric Study Group, Auckland, July 1998.
    as

    Download full text from publisher

    File URL: https://www.uvic.ca/socialsciences/economics/_assets/docs/econometrics/wp98-02.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Unit Root Tests With Missing Observations
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-04-02 01:13:00

    Citations

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


    Cited by:

    1. Facchini, François & Melki, Mickaël, 2013. "Efficient government size: France in the 20th century," European Journal of Political Economy, Elsevier, vol. 31(C), pages 1-14.
    2. Chad Stroomer & David E.A. Giles, 2003. "Income Convergence and trade Openness: Fuzzy Clustering and Time Series Evidence," Econometrics Working Papers 0304, Department of Economics, University of Victoria.
    3. Mishra, Vinod & Smyth, Russell, 2010. "Female labor force participation and total fertility rates in the OECD: New evidence from panel cointegration and Granger causality testing," Journal of Economics and Business, Elsevier, vol. 62(1), pages 48-64, January.
    4. Eberhardt, Markus & Teal, Francis, 2008. "Modeling technology and technological change in manufacturing: how do countries differ?," MPRA Paper 10690, University Library of Munich, Germany.
    5. Robert N. Collender & Samantha Roberts & Valerie L. Smith, 2007. "Signals from the Markets for Fannie Mae and Freddie Mac Subordinated Debt," FHFA Staff Working Papers 07-04, Federal Housing Finance Agency.
    6. Daniel L. Millimet & Ian K. McDonough, 2017. "Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 725-743, June.
    7. Bishai, David & Opuni, Marjorie & Poon, Andrew, 2007. "Does the level of infant mortality affect the rate of decline?: Time series data from 21 countries," Economics & Human Biology, Elsevier, vol. 5(1), pages 74-81, March.
    8. Papana, Angeliki & Kyrtsou, Catherine & Kugiumtzis, Dimitris & Diks, Cees, 2017. "Financial networks based on Granger causality: A case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 65-73.
    9. Chris Parker & Kamalini Ramdas & Nicos Savva, 2016. "Is IT Enough? Evidence from a Natural Experiment in India’s Agriculture Markets," Management Science, INFORMS, vol. 62(9), pages 2481-2503, September.
    10. J. C. H. Jones & J. A. Schofield & D. E. A. Giles, 2000. "Our fans in the north: the demand for British Rugby League," Applied Economics, Taylor & Francis Journals, vol. 32(14), pages 1877-1887.
    11. Mitchell, Karlyn & Pearce, Douglas K., 2007. "Professional forecasts of interest rates and exchange rates: Evidence from the Wall Street Journal's panel of economists," Journal of Macroeconomics, Elsevier, vol. 29(4), pages 840-854, December.
    12. Francis Teal & Markus Eberhardt, 2010. "Productivity Analysis in Global Manufacturing Production," Economics Series Working Papers 515, University of Oxford, Department of Economics.
    13. Warren Moraghen & Boopen Seetanah & Noor Ul Haq Sookia, 2023. "The impact of exchange rate and exchange rate volatility on Mauritius foreign direct investment: A sector‐wise analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 208-224, January.
    14. Md. Saifur Rahman & Farihana Shahari, 2020. "Economic Integration And Investment Opportunities: A Study On Asean+3 Countries," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 25, pages 69-91, June.
    15. Facchini, François & Melki, Mickaël, 2013. "Efficient government size: France in the 20th century," European Journal of Political Economy, Elsevier, vol. 31(C), pages 1-14.
    16. Marvasti, Akbar & Smyth, David, 2008. "Barter and Business Cycles: A Comment and Further Empirical Evidence," MPRA Paper 18258, University Library of Munich, Germany.

    More about this item

    Keywords

    Unit Roots; Dickey-Fuller Test; Missing Data;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:vic:vicewp:9802. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Kali Moon (email available below). General contact details of provider: https://edirc.repec.org/data/devicca.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.