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The ARAR Error Model for Univariate Time Series and Distributed Lag Models

Author

Listed:
  • R. A. L. Carter

    (University of Western Ontario and University of Calgary)

  • A. Zellner

    (University of Chicago)

Abstract

We show that the use of prior information derived from former empirical findings and/or subject matter theory regarding the lag structure of the observable variables together with an AR process for the error terms can produce univariate and single equation models that are intuitively appealing, simple to implement, and work well in practice.

Suggested Citation

  • R. A. L. Carter & A. Zellner, 2002. "The ARAR Error Model for Univariate Time Series and Distributed Lag Models," University of Western Ontario, Departmental Research Report Series 20025, University of Western Ontario, Department of Economics.
    • Handle: RePEc:uwo:uwowop:20025
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    References listed on IDEAS

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    7. Zellner, Arnold & Tobias, Justin, 2001. "Further Results on Bayesian Method of Moments Analysis of the Multiple Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(1), pages 121-140, February.
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    12. Zellner, Arnold & Hong, Chansik & Min, Chung-ki, 1991. "Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 275-304.
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    Cited by:

    1. Richard Carter & Arnold Zellner, 2003. "AR Versus MA Disturbance Terms," Economics Bulletin, AccessEcon, vol. 3(21), pages 1-3.
    2. Spyros Makridakis & Andreas Merikas & Anna Merika & Mike G. Tsionas & Marwan Izzeldin, 2020. "A novel forecasting model for the Baltic dry index utilizing optimal squeezing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 56-68, January.
    3. repec:ebl:ecbull:v:3:y:2003:i:21:p:1-3 is not listed on IDEAS

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