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Common Persistence in Nonlinear Autoregressive Models

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Author Info
Boswijk, H.P.
Franses, P.H.

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Abstract

In this paper we propose to consider a measure of the persistence of shocks in linear combinations of nonlinear processes, in order to investigate the possible presence of common long-run properties. We argue that such common persistence for nonlinear time series corresponds to the concept of cointegration for linear time series. Additionally, persistence can be used to examine other common properties such as nonlinearity, which in turn may be used to identify simplifying structures. The calculation of our persistence measure is based on Monte Carlo integration. An application to logistic smooth transition autoregressive models and artificial neural networks for industrial production in Belgium, Germany and the USA yields that Belgium and Germany display common persistence, while the USA - Germany relation seems to be much weaker. In addition, we find that the Germany and USA data display common nonlinearity.

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Publisher Info
Paper provided by Erasmus University of Rotterdam - Econometric Institute in its series Papers with number 9702/a.

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Length: 11 pages
Date of creation: 1997
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Handle: RePEc:fth:erroem:9702/a

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Related research
Keywords: TIME SERIES ; ECONOMIC MODELS;

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Find related papers by JEL classification:
C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Franses, P.H. & Draisma, G., 1995. "Recognizing Changing Seasonal Patterns Using Artificial Neural Networks," Papers 9514/a, Erasmus University of Rotterdam - Econometric Institute.
  2. Priestley, M. B., 1988. "Current developments in time series modelling," Journal of Econometrics, Elsevier, vol. 37(1), pages 67-86, January. [Downloadable!] (restricted)
  3. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-64, Oct.-Dec.. [Downloadable!] (restricted)
  4. Pesaran, M. Hashem & Shin, Yongcheol, 1996. "Cointegration and speed of convergence to equilibrium," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 117-143. [Downloadable!] (restricted)
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  5. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De. [Downloadable!] (restricted)
  6. Lin, Chien-Fu Jeff & Terasvirta, Timo, 1994. "Testing the constancy of regression parameters against continuous structural change," Journal of Econometrics, Elsevier, vol. 62(2), pages 211-228, June. [Downloadable!] (restricted)
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  1. R. Paap & Ph.H.B.F. Franses, 1999. "Does the US and Canada have a common nonlinear cycle in unemployment?," Econometric Institute Report 108, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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