IDEAS home Printed from https://ideas.repec.org/a/erh/journl/v11y2019i1p24-38.html
   My bibliography  Save this article

Learning from Errors While Forecasting Inflation: A Case for Intercept Correction

Author

Listed:
  • Muhammad Jahanzeb Malik

    (State Bank of Pakistan.)

  • Muhammad Nadim Hanif

    (State Bank of Pakistan.)

Abstract

Structural changes are quite common in macroeconomic time series. Moreover, any underlying macroeconomic relationship cannot be correctly specified unless we know the true model. Structural changes in time series and misspecification in empirical model are observed as shifts in the constant of the underlying relationship between the subject variables of interest. Forecasting from such a model assuming 'no structural break' and 'correct model' is tantamount to ignoring important aspects of underlying economy and mostly results in forecast failure(s). Intercept correction (IC) is a method for accommodating such ignored structural break(s) and omitted variable(s). We use a simple model (for July 1991 to March 2016) to forecast inflation for 25 countries and compare its performance with a) the same model with optimal IC, b) the same model with half IC, and c) a random walk model. Optimal IC approach, though computational intensive, outperforms in forecasting next period inflation compared to one from a) the same model without IC, b) the same model with half intercept correction, and c) random walk model without IC. For the particular class of inflation models under study, over the time period specified, 'quarter IC' works best among the fixed IC rules.

Suggested Citation

  • Muhammad Jahanzeb Malik & Muhammad Nadim Hanif, 2019. "Learning from Errors While Forecasting Inflation: A Case for Intercept Correction," International Econometric Review (IER), Econometric Research Association, vol. 11(1), pages 24-38, April.
    • Handle: RePEc:erh:journl:v:11:y:2019:i:1:p:24-38
    as

    Download full text from publisher

    File URL: http://www.era.org.tr/makaleler/304468.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muhammad Nadim Hanif & Muhammad Jahanzeb Malik, 2015. "Evaluating the Performance of Inflation Forecasting Models of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 43-78.
    2. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    3. Clements, Michael P & Hendry, David F, 1996. "Intercept Corrections and Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 475-494, Sept.-Oct.
    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. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    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. Brüggemann, Ralf & Lütkepohl, Helmut, 2013. "Forecasting contemporaneous aggregates with stochastic aggregation weights," International Journal of Forecasting, Elsevier, vol. 29(1), pages 60-68.
    4. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29, January.
    5. Fokin, Nikita, 2021. "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 5-29.
    6. Richard H. Clarida & Lucio Sarno & Mark P. Taylor & Giorgio Valente, 2006. "The Role of Asymmetries and Regime Shifts in the Term Structure of Interest Rates," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1193-1224, May.
    7. Spencer, Peter & Liu, Zhuoshi, 2010. "An open-economy macro-finance model of international interdependence: The OECD, US and the UK," Journal of Banking & Finance, Elsevier, vol. 34(3), pages 667-680, March.
    8. Irfan Civcir, 2003. "The Monetary Models of the Turkish Lira/U.S. Dollar Exchange Rate: Long-run Relationships, Short-run Dynamics, and Forecasting," Eastern European Economics, Taylor & Francis Journals, vol. 41(6), pages 43-63, January.
    9. Khan, Safdar Ullah & Saqib, Omar Farooq, 2011. "Political instability and inflation in Pakistan," Journal of Asian Economics, Elsevier, vol. 22(6), pages 540-549.
    10. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    11. Aaron Smith, 2005. "Forecasting in the presence of level shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 557-574.
    12. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    13. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.
    14. Jorge Luis Tonetto & Adelar Fochezatto & Josep Miquel Pique, 2023. "The Impact of the COVID-19 Pandemic on the Use of the Menor Preço Brasil Application," Administrative Sciences, MDPI, vol. 13(11), pages 1-19, October.
    15. Carlos A. Medel, 2018. "A Comparison Between Direct and Indirect Seasonal Adjustment of the Chilean GDP 1986–2009 with X-12-ARIMA," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 47-87, April.
    16. 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.
    17. Celia Rangel-Pérez & Belen López & Manuel Fernández, 2024. "A strategic sustainability model for global luxury companies in the management of CO2 emissions," International Entrepreneurship and Management Journal, Springer, vol. 20(3), pages 1597-1615, September.
    18. Issler, João Victor & Lima, Luiz Renato, 2009. "A panel data approach to economic forecasting: The bias-corrected average forecast," Journal of Econometrics, Elsevier, vol. 152(2), pages 153-164, October.
    19. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    20. repec:zbw:bofitp:2015_012 is not listed on IDEAS
    21. Wagatha, Matthias, 2007. "Integration, Kointegration und die Langzeitprognose von Kreditausfallzyklen [Integration, Cointegration and Long-Horizont Forecasting of Credit-Default-Cycles]," MPRA Paper 8602, University Library of Munich, Germany.

    More about this item

    Keywords

    Forecasting; Structural changes; Intercept correction; Misspecification; Inflation models.;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:erh:journl:v:11:y:2019:i:1:p:24-38. 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: M. F. Cosar (email available below). General contact details of provider: https://edirc.repec.org/data/eratrea.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.