IDEAS home Printed from
   My bibliography  Save this paper

Evaluating Performance of Inflation Forecasting Models of Pakistan


  • Hanif, Muhammad Nadim
  • Malik, Muhammad Jahanzeb


This study compares the forecasting performance of various models of inflation for a developing country estimated over the period of last two decades. Performance is measured at different forecast horizons (up to 24 months ahead) and for different time periods when inflation is low, high and moderate (in the context of Pakistan economy). Performance is considered relative to the best amongst the three usually used forecast evaluation benchmarks – random walk, ARIMA and AR(1) models. We find forecasts from ARDL modeling and certain combinations of point forecasts better than the best benchmark model, the random walk model, as well as structural VAR and Bayesian VAR models for forecasting inflation for Pakistan. For low inflation regime, upper trimmed average of the point forecasts out performs any model based forecasting for short period of time. For longer period, use of an ARDL model is the best choice. For moderate inflation regime different ways to average various models’ point forecasts turn out to be the best for all inflation forecasting horizons. The most important case of high inflation regime was best forecasted by ARDL approach for all the periods up to 24 months ahead. In overall, we can say that forecasting performance of different approaches is state dependent for the case of developing countries, like Pakistan, where inflation is occasionally high and volatile.

Suggested Citation

  • Hanif, Muhammad Nadim & Malik, Muhammad Jahanzeb, 2015. "Evaluating Performance of Inflation Forecasting Models of Pakistan," MPRA Paper 66843, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:66843

    Download full text from publisher

    File URL:
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    1. Brent Meyer & Guhan Venkatu, 2012. "Trimmed-mean inflation statistics: just hit the one in the middle," Working Paper 1217, Federal Reserve Bank of Cleveland, revised 01 Feb 2014.
    2. Edda Claus & Iris Claus, 2002. "How many jobs? A leading indicator model of New Zealand employment," Treasury Working Paper Series 02/13, New Zealand Treasury.
    3. Hanif, Muhammad N. & Batool, Irem, 2006. "Openness and Inflation: A Case Study of Pakistan," MPRA Paper 10214, University Library of Munich, Germany.
    4. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    5. Madhavi Bokil & Axel Schimmelpfennig, 2006. "Three Attempts at Inflation Forecasting in Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 45(3), pages 341-368.
    6. S. Adnan H. A. S. Bukhari & Safdar Ullah Khan, 2008. "Estimating Output Gap for Pakistan Economy: Structural and Statistical Approaches," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 4, pages 31-60.
    7. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    8. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    9. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    10. Öğünç, Fethi & Akdoğan, Kurmaş & Başer, Selen & Chadwick, Meltem Gülenay & Ertuğ, Dilara & Hülagü, Timur & Kösem, Sevim & Özmen, Mustafa Utku & Tekatlı, Necati, 2013. "Short-term inflation forecasting models for Turkey and a forecast combination analysis," Economic Modelling, Elsevier, vol. 33(C), pages 312-325.
    11. Muhammad Ali Choudhary & Muhammad Nadim Hanif & Sajawal Khan & Muhammad Rehman, 2012. "Procyclical Monetary Policy and Governance," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 8, pages 33-43.
    12. Michael P. Clements & David F.Hendry, 2001. "Forecasting with difference-stationary and trend-stationary models," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-19.
    13. Choudhary, M. Ali & Naeem, Saima & Faheem, Abdul & Hanif, Nadim & Pasha, Farooq, 2011. "Formal sector price discoveries: preliminary results from a developing country," MPRA Paper 32368, University Library of Munich, Germany.
    14. David Norman & Anthony Richards, 2012. "The Forecasting Performance of Single Equation Models of Inflation," The Economic Record, The Economic Society of Australia, vol. 88(280), pages 64-78, March.
    15. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers CWP41/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Ali Choudhary & Amjad Ali & Shah Hussain & Vasco J. Gabriel, 2012. "Bank Lending and Monetary Shocks: Evidence from a Developing Economy," SBP Working Paper Series 45, State Bank of Pakistan, Research Department.
    17. Abdul Qayyum & Sajawal Khan & Idrees Khawaja, 2005. "Interest Rate Pass-through in Pakistan: Evidence from Transfer Function Approach," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 44(4), pages 975-1001.
    18. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, C.E.P.R. Discussion Papers.
    19. Khan, Mahmood ul Hassan & Hanif, Muhammad Nadim, 2012. "Role of Demand and Supply Shocks in Driving Inflation: A Case Study of Pakistan," MPRA Paper 48884, University Library of Munich, Germany.
    20. Hanif, Muhammad Nadim, 2012. "A Note on Food Inflation in Pakistan," MPRA Paper 45009, University Library of Munich, Germany, revised 11 Mar 2013.
    21. Hamilton, James D & Herrera, Ana Maria, 2004. "Oil Shocks and Aggregate Macroeconomic Behavior: The Role of Monetary Policy: Comment," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(2), pages 265-286, April.
    22. Konstantins Benkovskis, 2008. "Short-Term Forecasts of Latvia's Real Gross Domestic Product Growth Using Monthly Indicators," Working Papers 2008/05, Latvijas Banka.
    23. Jan-Erik Antipin & Farid Jimmy Boumediene & Pär Österholm, 2014. "Forecasting Inflation Using Constant Gain Least Squares," Australian Economic Papers, Wiley Blackwell, vol. 53(1-2), pages 2-15, June.
    24. Fayyaz Hussain & Constance Kabibi Kimuli, 2012. "Determinants of Foreign Direct Investment to Developing Countries," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 8, pages 13-31.
    25. Faiz Bilquees, 1988. "Inflation in Pakistan: Empirical Evidence on the Monetarist and Structuralist Hypotheses," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 27(2), pages 109-129.
    26. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    27. Haider, Adnan & Hanif, Muhammad Nadeem, 2007. "Inflation Forecasting in Pakistan using Artificial Neural Networks," MPRA Paper 14645, University Library of Munich, Germany.
    28. Gabriel Caldas Montes & Caroline Cabral Machado, 2013. "Credibility and the credit channel transmission of monetary policy theoretical model and econometric analysis for Brazil," Journal of Economic Studies, Emerald Group Publishing, vol. 40(4), pages 469-492, August.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Aadil Nakhoda, 2014. "The Influence of Industry Financial Composition on the Exports from Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 10, pages 21-49.
    2. repec:pid:journl:v:55:y:2016:i:3:p:211-225 is not listed on IDEAS

    More about this item


    Inflation; Forecast Evaluation; Random Walk model; AR(1) model; ARIMA model; ARDL model; Structural VAR model; Bayesian VAR model; Trimmed Average;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:66843. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter) or (Rebekah McClure). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.