IDEAS home Printed from
   My bibliography  Save this paper

Recursive estimation and modelling of nonstationary and nonlinear time series


  • Peter C. Young
  • David E. Runkle


This paper presents a unified approach to nonlinear and nonstationary time-series analysis for a fairly wide class of linear time variable parameter (TVP) or nonlinear systems. The method theory exploits recursive filtering and fixed interval smoothing algorithms to derive TVP linear model approximations to the nonlinear or nonstationary stochastic system, on the basis of data obtained from the system during planned experiments or passive monitoring exercises. This TVP model includes the State Dependent type of Model (SDM) as a special case, and two particular SDM forms, due to Priestly and Young, are discussed in detail. The paper concludes with three practical examples: the first based on the modelling of data from a simulated nonlinear growth equation; the second concerned with the adaptive forecasting and smoothing of the Box-Jenkins Airline Passenger data; and the third providing a critical appraisal of state dependent modelling applied to the famous Sunspot time-series.

Suggested Citation

  • Peter C. Young & David E. Runkle, 1989. "Recursive estimation and modelling of nonstationary and nonlinear time series," Discussion Paper / Institute for Empirical Macroeconomics 7, Federal Reserve Bank of Minneapolis.
  • Handle: RePEc:fip:fedmem:7

    Download full text from publisher

    File URL:
    Download Restriction: no

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-16.
    2. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Time-series analysis;


    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:fip:fedmem:7. 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: (Janelle Ruswick). 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.