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Forecasting irish inflation using ARIMA models


  • Meyler, Aidan
  • Kenny, Geoff
  • Quinn, Terry


This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models - the Box Jenkins approach and the objective penalty function methods. The emphasis is on forecast performance which suggests more focus on minimising out-of-sample forecast errors than on maximising in-sample ‘goodness of fit’. Thus, the approach followed is unashamedly one of ‘model mining’ with the aim of optimising forecast performance. Practical issues in ARIMA time series forecasting are illustrated with reference to the harmonised index of consumer prices (HICP) and some of its major sub-components.

Suggested Citation

  • Meyler, Aidan & Kenny, Geoff & Quinn, Terry, 1998. "Forecasting irish inflation using ARIMA models," MPRA Paper 11359, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:11359

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    References listed on IDEAS

    1. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Bayesian VAR Models for Forecasting Irish Inflation," MPRA Paper 11360, University Library of Munich, Germany.
    2. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    3. Michael F. Bryan & Stephen G. Cecchetti, 1994. "Measuring Core Inflation," NBER Chapters,in: Monetary Policy, pages 195-219 National Bureau of Economic Research, Inc.
    4. Stephen G. Cecchetti, 1995. "Inflation Indicators and Inflation Policy," NBER Chapters,in: NBER Macroeconomics Annual 1995, Volume 10, pages 189-236 National Bureau of Economic Research, Inc.
    5. Martin S. Feldstein, 1997. "The Costs and Benefits of Going from Low Inflation to Price Stability," NBER Chapters,in: Reducing Inflation: Motivation and Strategy, pages 123-166 National Bureau of Economic Research, Inc.
    6. 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.
    7. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    8. Dotsey, Michael & Ireland, Peter, 1996. "The welfare cost of inflation in general equilibrium," Journal of Monetary Economics, Elsevier, vol. 37(1), pages 29-47, February.
    9. Víctor Gómez & Agustín Maravall, 1998. "Automatic Modeling Methods for Univariate Series," Working Papers 9808, Banco de España;Working Papers Homepage.
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    Cited by:

    1. Akhter, Tahsina, 2013. "Short-Term Forecasting of Inflation in Bangladesh with Seasonal ARIMA Processes," MPRA Paper 43729, University Library of Munich, Germany.
    2. KUMAR Manoj & ANAND Madhu, 2014. "An Application Of Time Series Arima Forecasting Model For Predicting Sugarcane Production In India," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 9(1), pages 81-94, April.
    3. Meyler, Aidan, 1999. "A Statistical Measure Of Core Inflation," Research Technical Papers 2/RT/99, Central Bank of Ireland.
    4. Aguilar, Ruben & Valdivia, Daney, 2011. "Precios de exportación de gas natural para Bolivia: Modelación y pooling de pronósticos
      [Bolivian natural gas export prices: Modeling and forecast pooling]
      ," MPRA Paper 35485, University Library of Munich, Germany.
    5. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Bayesian VAR Models for Forecasting Irish Inflation," MPRA Paper 11360, University Library of Munich, Germany.
    6. Quinn, Terry & Kenny, Geoff & Meyler, Aidan, 1999. "Inflation Analysis: An Overview," MPRA Paper 11361, University Library of Munich, Germany.
    7. Meyler, Aidan, 1999. "The Non-Accelerating Inflation Rate of Unemployment (NAIRU) in a Small Open Economy: The Irish Context," Research Technical Papers 5/RT/99, Central Bank of Ireland.
    8. Zafar, Raja Fawad & Qayyum, Abdul & Ghouri, Saghir Pervaiz, 2015. "Forecasting Inflation using Functional Time Series Analysis," MPRA Paper 67208, University Library of Munich, Germany.
    9. Jeff Tayman & Stanley Smith & Jeffrey Lin, 2007. "Precision, bias, and uncertainty for state population forecasts: an exploratory analysis of time series models," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(3), pages 347-369, June.
    10. repec:asg:wpaper:1014 is not listed on IDEAS
    11. Gatt, William, 2013. "Forecasting inflation at the Central Bank of Malta�," MPRA Paper 56876, University Library of Munich, Germany.
    12. repec:asg:wpaper:1027 is not listed on IDEAS
    13. 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.
    14. Andreja Pufnik & Davor Kunovac, 2006. "Short-Term Forecasting of Inflation in Croatia with Seasonal ARIMA Processes," Working Papers 16, The Croatian National Bank, Croatia.
    15. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
    16. Han Hwa Goh & Kim Leng Tan & Chia Ying Khor & Sew Lai Ng, 2016. "Volatility and Market Risk of Rubber Price in Malaysia: Pre- and Post-Global Financial Crisis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 323-344, December.
    17. Roma, Moreno & Skudelny, Frauke & Benalal, Nicholai & Diaz del Hoyo, Juan Luis & Landau, Bettina, 2004. "To aggregate or not to aggregate? Euro area inflation forecasting," Working Paper Series 374, European Central Bank.
    18. Musa Y., 2014. "Modeling an Average Monthly Temperature of Sokoto Metropolis Using Short Term Memory Models," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 4(7), pages 382-397, July.

    More about this item


    NAIRU; inflation; unobserved components; kalman filter;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium


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