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A smooth permanent surge process

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Author Info
González Gómez, Andrés () (Dept. of Economic Statistics, Stockholm School of Economics)

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Abstract

In this paper we introduce the Smooth Permanent Surge [SPS] model. The model is an integrated non lineal moving average process with possibly unit roots in the moving average coefficients. The process nests the Stochastic Permanent Break [STOPBREAK] process by Engle and Smith (1999) and in a limiting case it converges to Threshold Integrated Moving Average [TIMA] models by Gonzalo and Martinez (2003). A test of SPS against STOPBREAK process is presented. Additionally, we introduce a new test for testing SPS process against the random walk. The small sample properties of these tests are

investigated by Monte Carlo experiments. An application to the stock markets is presented.

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Publisher Info
Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 572.

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Length: 29 pages
Date of creation: 07 Dec 2004
Date of revision:
Handle: RePEc:hhs:hastef:0572

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Related research
Keywords: Linearity test; Monte Carlo testing; Smooth transitions; Moving Averages Models; Permanent Shock; Transitory Shocks.;

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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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. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November. [Downloadable!] (restricted)
    Other versions:
  2. Oscar Martin & Jesus Gonzalo, 2004. "Threshold Integrated Moving Average Models (Does Size Matter? Maybe So)," Econometric Society 2004 North American Winter Meetings 145, Econometric Society.
  3. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August. [Downloadable!] (restricted)
    Other versions:
  4. Granger, Clive W. J. & Swanson, Norman R., 1997. "An introduction to stochastic unit-root processes," Journal of Econometrics, Elsevier, vol. 80(1), pages 35-62, September. [Downloadable!] (restricted)
    Other versions:
  5. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September. [Downloadable!] (restricted)
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  6. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99. [Downloadable!] (restricted)
  7. Jansen, Eilev S & Terasvirta, Timo, 1996. "Testing Parameter Constancy and Super Exogeneity in Econometric Equations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 735-63, November.
    Other versions:
  8. Andrews, Donald W. K., 1987. "Laws of Large Numbers for Dependent Non-Identically Distributed Random Variables," Working Papers 645, California Institute of Technology, Division of the Humanities and Social Sciences. [Downloadable!]
  9. Robert F. Engle & Aaron D. Smith, 1999. "Stochastic Permanent Breaks," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 553-574, November. [Downloadable!] (restricted)
    Other versions:
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