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

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  • Tal Boger

    (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

  • Tal Boger, 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
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    File URL: https://sites.krieger.jhu.edu/iae/files/2018/09/Boger-Iran-ML-Inflation.pdf
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    References listed on IDEAS

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    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.
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