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Inflation in Pakistan: High-Frequency Estimation and Forecasting

Author

Listed:
  • Sonan Memon

    (Pakistan Institute of Development Economics)

Abstract

I begin by motivating the utility of high-frequency inflation estimation and reviewing recent work done at the State Bank of Pakistan for inflation forecasting and now-casting GDP using machine learning (ML) tools. I also present stylised facts about the structure of historical and especially recent inflation trends in Pakistan.

Suggested Citation

  • Sonan Memon, 2022. "Inflation in Pakistan: High-Frequency Estimation and Forecasting," PIDE-Working Papers 2022:12, Pakistan Institute of Development Economics.
  • Handle: RePEc:pid:wpaper:2022:12
    as

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    References listed on IDEAS

    as
    1. Shahzad Ahmad & Adnan Haider, 2019. "An evaluation of the forecast performance of DSGE and VAR Models: The case of a developing country," Business Review, School of Economics and Social Sciences, IBA Karachi, vol. 14(1), pages 28-52, January-J.
    2. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    3. Carola Conces Binder, 2021. "Political Pressure on Central Banks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 715-744, June.
    4. Allison Koenecke & Hal Varian, 2020. "Synthetic Data Generation for Economists," Papers 2011.01374, arXiv.org, revised Nov 2020.
    5. Burton A. Abrams & James L. Butkiewicz, 2012. "The Political Business Cycle: New Evidence from the Nixon Tapes," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(2‐3), pages 385-399, March.
    6. 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.
    7. Beck, Guenter W. & Kotz, Hans-Helmut & Zabelina, Natalia, 2020. "Price gaps at the border: Evidence from multi-country household scanner data," Journal of International Economics, Elsevier, vol. 127(C).
    8. Cavallo, Alberto & Kryvtsov, Oleksiy, 2023. "What can stockouts tell us about inflation? Evidence from online micro data," Journal of International Economics, Elsevier, vol. 146(C).
    9. Ateeb Akhter Shah Syed & Kevin Haeseung Lee, 2021. "Macroeconomic forecasting for Pakistan in a data-rich environment," Applied Economics, Taylor & Francis Journals, vol. 53(9), pages 1077-1091, February.
    10. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    11. Cukierman, Alex & Webb, Steven B & Neyapti, Bilin, 1992. "Measuring the Independence of Central Banks and Its Effect on Policy Outcomes," The World Bank Economic Review, World Bank, vol. 6(3), pages 353-398, September.
    12. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    13. Sergey I. Nikolenko, 2021. "Synthetic Data for Deep Learning," Springer Optimization and Its Applications, Springer, number 978-3-030-75178-4, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Forecast Accuracy; Forecasts of Inflation in Pakistan; High Frequency; Hyperinflation; Inflation Estimation and Forecasting; Machine Learning; Synthetic Data; VAR Models; Web Scrapping and Scanner Data;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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