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Inflation in Pakistan: High Frequency Estimation and Forecasting

Published in , 2022

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. However, since the available data and already used methods cannot achieve high frequency forecasting, I discuss three novel techniques from recent literature, including web scrapping, scanner data and synthetic data. Due to a lack of access to Pakistan’s scanner and web-scrapped data, I generate synthetic data using generative ML models (Gaussian Copula and PAR models) and numerical analysis (cubic spline interpolation) methods. I use cubic splines to estimate the monthly inflation rate from quarterly data and unknown high frequency, weekly inflation rate from actual monthly data. Meanwhile, I use a probabilistic autoregressive ML model to forecast future short-run inflation for Pakistan from 2020 to 2023. I evaluate the accuracy of ML forecasts by comparing them with forecast error variances and predictions from conventional reduced form vector autoregressive models (VAR).

Recommended citation: Memon, S. (2022). Inflation in Pakistan: High-Frequency Estimation and Forecasting (2022: 12). Pakistan Institute of Development Economics.
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