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A Thick ANN Model for Forecasting Inflation

Author

Listed:
  • Muhammad Nadim Hanif

    (State Bank of Pakistan)

  • Khurrum S. Mughal

    (State Bank of Pakistan)

  • Javed Iqbal

    (State Bank of Pakistan)

Abstract

Inflation forecasting is an essential activity at central banks to formulate forward looking monetary policy stance. Like in other fields, machine learning is finding its way to forecasting; inflation forecasting is not any exception. In machine learning, most popular tool for forecasting is artificial neural network (ANN). Researchers have used different performance measures (including RMSE) to optimize set of characteristics - architecture, training algorithm and activation function - of an ANN model. However, any chosen ‘optimal’ set may not remain reliable on realization of new data. We suggest use of ‘mode’ or most appearing set from a simulation based distribution of optimum ‘set of characteristics of ANN model’; selected from a large number of different sets. Here again, we may have a different trained network in case we re-run this ‘modal’ optimal set since initial weights in training process are assigned randomly. To overcome this issue, we suggest use of ‘thickness’ to produce stable and reliable forecasts using modal optimal set. Using January 1958 to December 2017 year on year (YoY) inflation data of Pakistan, we found that our YoY inflation forecasts (based on aforementioned multistage forecasting scheme) outperform those from a number of inflation forecasting models of Pakistan economy.

Suggested Citation

  • Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
  • Handle: RePEc:sbp:wpaper:99
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    File URL: http://www.sbp.org.pk/publications/wpapers/2018/wp99.pdf
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    References listed on IDEAS

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    Cited by:

    1. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.

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    More about this item

    Keywords

    Artificial Neural Networks; Inflation Forecasting;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • 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

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