IDEAS home Printed from https://ideas.repec.org/p/nip/nipewp/2-2000.html
   My bibliography  Save this paper

The Forecast Performance of Long Memory and Markov Switching Models

Author

Listed:
  • Vasco J. Gabriel

    (Universidade do Minho - NIPE
    Birkbeck College, University of London)

  • Luis F. Martins

    (Instituto Superior de Ciências do Trabalho e da Empresa, UNIDE)

Abstract

Recent research has focused on the links between long memory and structural change, stressing the long memory properties that may arise in models with parameter changes. In this paper, we contribute to this research by comparing the forecasting abilities of long memory and Markov switching models. Two approaches are employed: a Monte Carlo study and an empirical comparison, using the quarterly Consumer Price inflation rate in Portugal in the period 1968-1998. Although long memory models may capture some in-sample features of the data, when shifts occur in the series considered, their forecast performance is relatively poor, when compared with simple linear and Markov switching models. Moreover, our findings, in a more general framework, are in accordance with the works of Clements and Hendry (1998) and Clements and Krolzig (1998), reinforcing the idea that simple linear time series models remain useful tools for prediction.

Suggested Citation

  • Vasco J. Gabriel & Luis F. Martins, 2000. "The Forecast Performance of Long Memory and Markov Switching Models," NIPE Working Papers 2/2000, NIPE - Universidade do Minho.
  • Handle: RePEc:nip:nipewp:2/2000
    as

    Download full text from publisher

    File URL: http://www3.eeg.uminho.pt/economia/nipe/docs/2000/NIPE_WP_2_2000.PDF
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    2. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
    3. Evans, Martin & Wachtel, Paul, 1993. "Inflation Regimes and the," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 25(3), pages 475-511, August.
    4. Timmermann, Allan, 2000. "Moments of Markov switching models," Journal of Econometrics, Elsevier, vol. 96(1), pages 75-111, May.
    5. Philip Hans Franses & Marius Ooms & Charles S. Bos, 1999. "Long memory and level shifts: Re-analyzing inflation rates," Empirical Economics, Springer, vol. 24(3), pages 427-449.
    6. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    7. Garcia, Rene & Perron, Pierre, 1996. "An Analysis of the Real Interest Rate under Regime Shifts," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 111-125, February.
    8. Granger, Clive W. J. & Terasvirta, Timo, 1999. "A simple nonlinear time series model with misleading linear properties," Economics Letters, Elsevier, vol. 62(2), pages 161-165, February.
    9. Michael P. Clements & Hans-Martin Krolzig, 1998. "A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 47-75.
    10. Nunes, Luis C & Newbold, Paul & Kuan, Chung-Ming, 1997. "Testing for Unit Roots with Breaks: Evidence on the Great Crash and the Unit Root Hypothesis Reconsidered," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(4), pages 435-448, November.
    11. Donald W. K. Andrews, 2003. "Tests for Parameter Instability and Structural Change with Unknown Change Point: A Corrigendum," Econometrica, Econometric Society, vol. 71(1), pages 395-397, January.
    12. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    13. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    14. Martin Evans & Paul Wachtel, 1993. "Inflation regimes and the sources of inflation uncertainty," Proceedings, Federal Reserve Bank of Cleveland, pages 475-520.
    15. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    16. Hidalgo, Javier & Robinson, Peter M., 1996. "Testing for structural change in a long-memory environment," Journal of Econometrics, Elsevier, vol. 70(1), pages 159-174, January.
    17. Granger, Clive W.J. & Hyung, Namwon, 1999. "Occasional Structural Breaks and Long Memory," University of California at San Diego, Economics Working Paper Series qt4d60t4jh, Department of Economics, UC San Diego.
    18. Ooms, M. & Doornik, J.A., 1999. "Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK inflation," Econometric Institute Research Papers EI 9947/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    2. van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.
    3. Laura Mayoral, 2005. "Is the observed persistence spurious? A test for fractional integration versus short memory and structural breaks," Economics Working Papers 956, Department of Economics and Business, Universitat Pompeu Fabra.
    4. C.S. Bos & S.J. Koopman & M. Ooms, 2007. "Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks," Tinbergen Institute Discussion Papers 07-099/4, Tinbergen Institute.
    5. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    6. Yigit, Taner M., 2010. "Inflation targeting: An indirect approach to assess the direct impact," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1357-1368, November.
    7. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    8. Sharon Kozicki & Peter A. Tinsley, "undated". "Moving Endpoints in Macrofinance," Computing in Economics and Finance 1996 _058, Society for Computational Economics.
    9. Gadea, Maria Dolores & Sabate, Marcela & Serrano, Jose Maria, 2004. "Structural breaks and their trace in the memory: Inflation rate series in the long-run," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(2), pages 117-134, April.
    10. Juan J. Dolado & Jesús Gonzalo & Laura Mayoral, 2005. "What is what?: A simple time-domain test of long-memory vs. structural breaks," Economics Working Papers 954, Department of Economics and Business, Universitat Pompeu Fabra.
    11. Kozicki, Sharon & Tinsley, P. A., 2001. "Shifting endpoints in the term structure of interest rates," Journal of Monetary Economics, Elsevier, vol. 47(3), pages 613-652, June.
    12. Philip Hans Franses & Marius Ooms & Charles S. Bos, 1999. "Long memory and level shifts: Re-analyzing inflation rates," Empirical Economics, Springer, vol. 24(3), pages 427-449.
    13. Morana, Claudio, 2000. "Measuring core inflation in the euro area," Working Paper Series 36, European Central Bank.
    14. Georgios P. Kouretas & Mark E. Wohar, 2012. "The dynamics of inflation: a study of a large number of countries," Applied Economics, Taylor & Francis Journals, vol. 44(16), pages 2001-2026, June.
    15. Remzi Uctum, 2007. "Économétrie des modèles à changement de régimes : un essai de synthèse," L'Actualité Economique, Société Canadienne de Science Economique, vol. 83(4), pages 447-482.
    16. Belbute, José M. & Pereira, Alfredo M., 2020. "Reference forecasts for CO2 emissions from fossil-fuel combustion and cement production in Portugal," Energy Policy, Elsevier, vol. 144(C).
    17. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
    18. de Figueiredo, Erik Alencar, 2010. "Dynamics of regional unemployment rates in Brazil: Fractional behavior, structural breaks, and Markov switching," Economic Modelling, Elsevier, vol. 27(5), pages 900-908, September.
    19. Kozicki, Sharon & Tinsley, P.A., 2005. "Permanent and transitory policy shocks in an empirical macro model with asymmetric information," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1985-2015, November.
    20. Carlos Barros & Luis Gil-Alana, 2013. "Inflation Forecasting in Angola: A Fractional Approach," African Development Review, African Development Bank, vol. 25(1), pages 91-104.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    Statistics

    Access and download statistics

    Corrections

    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:nip:nipewp:2/2000. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: NIPE (email available below). General contact details of provider: https://edirc.repec.org/data/nipampt.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.