IDEAS home Printed from
   My bibliography  Save this paper

On the Choice of the Unit Period in Time Series Models


  • Peter Fuleky

    () (UHERO, and Department of Economics, University of Hawaii at Manoa)


When estimating the parameters of a process, researchers can choose the reference unit of time (unit period) for their study. Frequently, they set the unit period equal to the observation interval. However, I show that decoupling the unit period from the observation interval facilitates the comparison of parameter estimates across studies with different data sampling frequencies. If the unit period is standardized (for example annualized) across these studies, then the parameters will represent the same attributes of the underlying process, and their interpretation will be independent of the sampling frequency.

Suggested Citation

  • Peter Fuleky, 2011. "On the Choice of the Unit Period in Time Series Models," Working Papers 2011-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
  • Handle: RePEc:hae:wpaper:2011-4

    Download full text from publisher

    File URL:
    File Function: First version, 2011
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Lo, Andrew W., 1988. "Maximum Likelihood Estimation of Generalized Itô Processes with Discretely Sampled Data," Econometric Theory, Cambridge University Press, vol. 4(02), pages 231-247, August.
    2. Czellar, Veronika & Karolyi, G. Andrew & Ronchetti, Elvezio, 2007. "Indirect robust estimation of the short-term interest rate process," Journal of Empirical Finance, Elsevier, vol. 14(4), pages 546-563, September.
    3. Ball, Clifford A. & Torous, Walter N., 1996. "Unit roots and the estimation of interest rate dynamics," Journal of Empirical Finance, Elsevier, vol. 3(2), pages 215-238, June.
    4. Chan, K C, et al, 1992. " An Empirical Comparison of Alternative Models of the Short-Term Interest Rate," Journal of Finance, American Finance Association, vol. 47(3), pages 1209-1227, July.
    5. Bergstrom, A. R., 1988. "The History of Continuous-Time Econometric Models," Econometric Theory, Cambridge University Press, vol. 4(03), pages 365-383, December.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Carl Bonham & Peter Fuleky & James Jones & Ashley Hirashima, 2015. "Nowcasting Tourism Industry Performance Using High Frequency Covariates," Working Papers 2015-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    2. Hendrik Thiel & Stephan L. Thomsen, 2015. "Individual Poverty Paths and the Stability of Control-Perception," SOEPpapers on Multidisciplinary Panel Data Research 794, DIW Berlin, The German Socio-Economic Panel (SOEP).
    3. Hirashima, Ashley & Jones, James & Bonham, Carl S. & Fuleky, Peter, 2017. "Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism," Annals of Tourism Research, Elsevier, vol. 63(C), pages 191-202.
    4. Millimet, Daniel L. & McDonough, Ian K., 2013. "Dynamic Panel Data Models with Irregular Spacing: With Applications to Early Childhood Development," IZA Discussion Papers 7359, Institute for the Study of Labor (IZA).

    More about this item


    Unit Period; Sampling Frequency; Bias; Time Series.;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:hae:wpaper:2011-4. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (UHERO). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.