IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/23856.html
   My bibliography  Save this paper

Forecasting methods: a comparative analysis

Author

Listed:
  • Iqbal, Javed

Abstract

Forecasting is an important tool for management, planning and administration in various fields. In this paper forecasting performance of different methods is considered using time series data of Pakistan's export to United Sates and money supply. It is found that, like other studies of this nature, no single forecasting method provides better forecast for both the series. The techniques considered are ARIMA, Regression Analysis, Vector Autoregression (VAR), Error Correction Model (ECM) and ARCH/GARCH models.

Suggested Citation

  • Iqbal, Javed, 2001. "Forecasting methods: a comparative analysis," MPRA Paper 23856, University Library of Munich, Germany, revised 2001.
    • Handle: RePEc:pra:mprapa:23856
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/23856/1/MPRA_paper_23856.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    2. LeSage, James P, 1990. "A Comparison of the Forecasting Ability of ECM and VAR Models," The Review of Economics and Statistics, MIT Press, vol. 72(4), pages 664-671, November.
    3. Manmohan S. Kumar, 1992. "The Forecasting Accuracy of Crude Oil Futures Prices," IMF Staff Papers, Palgrave Macmillan, vol. 39(2), pages 432-461, June.
    4. Michael F. Bleaney, 1998. "Exchange Rate Forecasts at Long Horizons: Are Error-Correction Models Superior?," Canadian Journal of Economics, Canadian Economics Association, vol. 31(4), pages 852-864, November.
    5. Lin Chan, Hing & Kam Lee, Shu, 1997. "Modelling and forecasting the demand for coal in China," Energy Economics, Elsevier, vol. 19(3), pages 271-287, July.
    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. Stevanovic Dalibor, 2016. "Common time variation of parameters in reduced-form macroeconomic models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 159-183, April.
    2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    3. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    4. Pooyan Amir-Ahmadi & Christian Matthes & Mu-Chun Wang, 2020. "Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 124-136, January.
    5. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    6. Chambers, Marcus J. & Ercolani, Joanne S. & Taylor, A.M. Robert, 2014. "Testing for seasonal unit roots by frequency domain regression," Journal of Econometrics, Elsevier, vol. 178(P2), pages 243-258.
    7. Vicente Esteve & Manuel Navarro-Ibáñez & María A. Prats, 2013. "The present value model of US stock prices revisited: long-run evidence with structural breaks, 1871-2010," Working Papers 04/13, Instituto Universitario de Análisis Económico y Social.
    8. Nyakabawo, Wendy & Miller, Stephen M. & Balcilar, Mehmet & Das, Sonali & Gupta, Rangan, 2015. "Temporal causality between house prices and output in the US: A bootstrap rolling-window approach," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 55-73.
    9. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    10. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. John M. Maheu & Stephen Gordon, 2008. "Learning, forecasting and structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 553-583.
    12. Benati, Luca, 2007. "Drift and breaks in labor productivity," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2847-2877, August.
    13. Paras Sachdeva & Wasim Ahmad & N. R. Bhanumurthy, 2023. "Uncovering time variation in public expenditure multipliers: new evidence," Indian Economic Review, Springer, vol. 58(2), pages 445-483, September.
    14. Jochmann Markus & Koop Gary, 2015. "Regime-switching cointegration," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 35-48, February.
    15. Wang, Chengjin & Ducruet, César, 2014. "Transport corridors and regional balance in China: the case of coal trade and logistics," Journal of Transport Geography, Elsevier, vol. 40(C), pages 3-16.
    16. Ireland, Peter N., 2004. "A method for taking models to the data," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1205-1226, March.
    17. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
    18. Kevin Lansing, 2009. "Time Varying U.S. Inflation Dynamics and the New Keynesian Phillips Curve," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 12(2), pages 304-326, April.
    19. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    20. Antonello D'Agostino & Paolo Surico, 2012. "A Century of Inflation Forecasts," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1097-1106, November.

    More about this item

    Keywords

    Forecasting Methods; Single Equation; Multiple Equations;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:pra:mprapa:23856. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.