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Inflation Forecasting for Pakistan in a Data-rich Environment (Article)

Author

Listed:
  • Syed Ateeb Akhter Shah

    (Deputy Director, State Bank of Pakistan, Karachi, Pakistan.)

  • Muhammad Ishtiaq

    (Assistant Director (on study leave), Monetary Policy Department, State Bank of Pakistan, Karachi, Pakistan, and Doctoral Graduate Assistant, Department of Economics, Western Michigan University, USA)

  • Sumbal Qureshi
  • Kaneez Fatima

    (Assistant Professor, Institute of Management Sciences, University of Balochistan, Quetta, Balochistan, Pakistan.)

Abstract

This paper uses machine learning methods to forecast the year-on-year CPI inflation of Pakistan and compare their forecasting performance against the comprehensive traditional forecasting suite contained in Hanif and Malik (2015). It also augments the comprehensive forecasting suite with the dynamic factor model which is able to handle a large amount of information and put all of these models in competition against the latest machine learning models. A set of 117 predictors covering a period of July 1995 to June 2020 is used for this purpose. We set the naïve mean model as the benchmark and compare its forecasting performance against 14 traditional and 5 sophisticated machine learning models. We forecast the year-on-year CPI inflation over a 24 months horizon. Forecasting performance is measured using the RMSE. Our results show that the machine learning approaches perform better than the traditional econometric models at 18 forecast horizons.

Suggested Citation

  • Syed Ateeb Akhter Shah & Muhammad Ishtiaq & Sumbal Qureshi & Kaneez Fatima, 2022. "Inflation Forecasting for Pakistan in a Data-rich Environment (Article)," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 61(4), pages 643-658.
    • Handle: RePEc:pid:journl:v:61:y:2022:i:4:p:643-658
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    More about this item

    Keywords

    Inflation; Pakistan; Classical Models; Machine Learning; LASSO; DFM;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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