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Modeling and Forecasting Pakistan´s Inflaction by Using Time Series Arima Models

Author

Listed:
  • Muhammad Abdus Salam

    (State Bank of Pakistan.)

  • Shazia Salam

    (Goverment Girls College Dargai. Pakistan.)

  • Mete Feridun

    (Cyprus International University. Nicosia,Cyprus.)

Abstract

This study attempts to outline the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Pakistan’s inflation. A framework for ARIMA forecasting is drawn up. On the basis of in-sample and out-of-sample forecast it can be concluded that the model has sufficient predictive powers and the findings are well in line with those of other studies. Further, in this study, the main focus is to forecast the monthly inflation on short-term basis, for this purpose, different ARIMA models are used and the candid model is proposed. On the basis of various diagnostic and selection & evaluation criteria the best and accurate model is selected for the short term forecasting of inflation.

Suggested Citation

  • Muhammad Abdus Salam & Shazia Salam & Mete Feridun, 2007. "Modeling and Forecasting Pakistan´s Inflaction by Using Time Series Arima Models," Economic Analysis Working Papers (2002-2010). Atlantic Review of Economics (2011-2016), Colexio de Economistas de A Coruña, Spain and Fundación Una Galicia Moderna, vol. 6, pages 1-10, February.
    • Handle: RePEc:eac:articl:01/06
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    References listed on IDEAS

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    1. Andrew Tylecote, 1981. "The Causes of the Present Inflation," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-349-06416-8, December.
    2. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
    3. Winters,L. Alan & Sapsford,David (ed.), 1990. "Primary Commodity Prices," Cambridge Books, Cambridge University Press, number 9780521385503, September.
    4. Stockton, David J & Glassman, James E, 1987. "An Evaluation of the Forecast Performance of Alternative Models of Inflation," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 108-117, February.
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    Cited by:

    1. Zafar, Raja Fawad & Qayyum, Abdul & Ghouri, Saghir Pervaiz, 2015. "Forecasting Inflation using Functional Time Series Analysis," MPRA Paper 67208, University Library of Munich, Germany.

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