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

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Author Info
R. A. L. Carter (University of Western Ontario and University of Calgary)
A. Zellner (University of Chicago)

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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.

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Publisher Info
Paper provided by University of Western Ontario, Department of Economics in its series UWO Department of Economics Working Papers with number 20025.

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Date of creation: 2002
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Handle: RePEc:uwo:uwowop:20025

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Postal: Department of Economics, Reference Centre, Social Science Centre, University of Western Ontario, London, Ontario, Canada N6A 5C2
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  1. Monahan, John F., 1983. "Fully Bayesian analysis of ARMA time series models," Journal of Econometrics, Elsevier, vol. 21(3), pages 307-331, April. [Downloadable!] (restricted)
  2. Phillips, P.C.B., 1986. "Testing for a Unit Root in Time Series Regression," Cahiers de recherche 8633, Universite de Montreal, Departement de sciences economiques.
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  3. Nicholls, D F & Pagan, Adrian R & Terrell, R D, 1975. "The Estimation and Use of Models with Moving Average Disturbance Terms: A Survey," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 16(1), pages 113-34, February. [Downloadable!] (restricted)
  4. 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 121-40, February.
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  5. Zellner, Arnold & Geisel, Martin S, 1970. "Analysis of Distributed Lag Models with Application to Consumption Function Estimation," Econometrica, Econometric Society, vol. 38(6), pages 865-88, November. [Downloadable!] (restricted)
  6. Phillips, P C B, 1991. "To Criticize the Critics: An Objective Bayesian Analysis of Stochastic Trends," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 333-64, Oct.-Dec.. [Downloadable!] (restricted)
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  7. Chib, Siddhartha, 1993. "Bayes regression with autoregressive errors : A Gibbs sampling approach," Journal of Econometrics, Elsevier, vol. 58(3), pages 275-294, August. [Downloadable!] (restricted)
  8. Schotman, Peter C & van Dijk, Herman K, 1991. "On Bayesian Routes to Unit Roots," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 387-401, Oct.-Dec.. [Downloadable!] (restricted)
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  9. 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. [Downloadable!] (restricted)
  10. Paul A. Samuelson, 1934. "A Synthesis of the Principle of Acceleration and the Multiplier," Journal of Political Economy, University of Chicago Press, vol. 47, pages 786. [Downloadable!] (restricted)
  11. Zellner, Arnold & Hong, Chansik, 1989. "Forecasting international growth rates using Bayesian shrinkage and other procedures," Journal of Econometrics, Elsevier, vol. 40(1), pages 183-202, January. [Downloadable!] (restricted)
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  12. Pantula, Sastry G & Gonzalez-Farias, Graciela & Fuller, Wayne A, 1994. "A Comparison of Unit-Root Test Criteria," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 449-59, October.
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