IDEAS home Printed from https://ideas.repec.org/p/ris/jhisae/0126.html
   My bibliography  Save this paper

Forecasting Inflation in Iran by Applying Maching Learning Algorithms to PPP Lag

Author

Listed:
  • Boger, Tal

    (The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise)

Abstract

Purchasing Power Parity (PPP) relates the prices of two countries by their exchange rates. Several economists use PPP to measure inflation in the absence of official and accurate government reports. In the case of Iran, the government’s official inflation figures are significantly lower than what one would expect given their economic troubles; therefore, we apply PPP to measure inflation in Iran. Because of its volatility in the short-run, PPP is often used as a long-run economic indicator. The main cause for this is that PPP is a leading indicator, creating short-term inaccuracies. However, using machine learning algorithms, we forecast both the time until there is zero PPP lag (i.e. the official and implied inflation rates are equal) and the difference between the official and implied inflation rate (allowing us to predict official inflation rates) for Iran with minimal volatility. This allows us to use PPP accurately over both the short- and long-run.

Suggested Citation

  • Boger, Tal, 2018. "Forecasting Inflation in Iran by Applying Maching Learning Algorithms to PPP Lag," Studies in Applied Economics 126, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    • Handle: RePEc:ris:jhisae:0126
    as

    Download full text from publisher

    File URL: https://sites.krieger.jhu.edu/iae/files/2018/09/Boger-Iran-ML-Inflation.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    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. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    2. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    3. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    4. Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
    5. Panos Constantinides & Ola Henfridsson & Geoffrey G. Parker, 2018. "Introduction—Platforms and Infrastructures in the Digital Age," Information Systems Research, INFORMS, vol. 29(2), pages 381-400, June.
    6. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    7. Galdo, Virgilio & Li, Yue & Rama, Martin, 2021. "Identifying urban areas by combining human judgment and machine learning: An application to India," Journal of Urban Economics, Elsevier, vol. 125(C).
    8. Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA 2021-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    9. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    10. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    11. Li, Jiyuan & Li, Zihui & Zhang, Min, 2023. "CFOs’ facial trustworthiness and bank loan contracts," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 332-357.
    12. Koffi Dumor & Li Yao, 2019. "Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis," Sustainability, MDPI, vol. 11(5), pages 1-22, March.
    13. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
    14. Michael Allan Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Papers 1906.03044, arXiv.org.
    15. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
    16. Onder Ozgur & Erdal Tanas Karagol & Fatih Cemil Ozbugday, 2021. "Machine learning approach to drivers of bank lending: evidence from an emerging economy," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-29, December.
    17. Diop, Thierno Bocar & Védrine, Lionel, 2025. "Did crop diversity criterion from CAP green payments affect both economic and environmental farm performances? Quasi-experimental evidence from France," Ecological Economics, Elsevier, vol. 227(C).
    18. Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    19. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    20. John Aoga & Juhee Bae & Stefanija Veljanoska & Siegfried Nijssen & Pierre Schaus, 2020. "Impact of weather factors on migration intention using machine learning algorithms," Papers 2012.02794, arXiv.org.

    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:ris:jhisae:0126. 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: Steve H. Hanke (email available below). General contact details of provider: https://edirc.repec.org/data/iaejhus.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.