Sonan Memon

Sonan Memon

I am an aspiring applied macroeconomist with interests in monetary economics and networks.

Publications

The Desirability of Economic Inequality: A Discourse.

Published in PIDE Working Paper, 2023

I critically examine the burgeoning literature on inequality in economics. I report major empirical stylized facts on wealth/income inequality in the world and specifically in Pakistan. I also discuss arguments presented by many economists and some philosophers regarding the undesirability of wealth and income inequality and policies recommended to reduce inequality. I present several arguments in defense of economic inequality by establishing its role in creating incentives, economic growth, and urbanization. I also argue that many of the policies proposed for ejecting inequality from the society impede on individual and social freedoms. Moreover, there are theoretical and philosophical conundrums regarding how to share the pie of wealth and income which stifle attempts to redistribute. Lastly, I ask the why question by interrogating the relevance of inequality, making the case that absolute poverty, pain and suffering are the relevant curses which have to be excommunicated from an ideal society rather than the distribution of wealth and income.

Recommended citation: Memon, S. (2022). The Desirability of Economic Inequality: A Discourse. PIDE Working Paper.
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Inflation in Pakistan: High-Frequency Estimation and Forecasting

Published in PIDE Working Paper Series, 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 (No. 2022: 12). Pakistan Institute of Development Economics.
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Machine Learning for Economists: An Introduction

Published in Pakistan Development Review, 2021

Machine Learning (henceforth ML) refers to the set of algorithms and computational methods which enable computers to learn patterns from training data without being explicitly programmed to do so. ML uses training data to learn patterns by estimating a mathematical model and making predictions in out of sample based on new or unseen input data. ML has the tremendous capacity to discover complex, flexible and crucially generalisable structure in training data. Conceptually speaking, ML can be thought of as a set of complex function approximation techniques which help us learn the unknown and potentially highly nonlinear mapping between the data and prediction outcomes, outperforming traditional techniques.

Recommended citation: Memon, S. (2021). Machine Learning for Economists. The Pakistan Development Review, 60(2), 201-211.
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Income inequality and macroeconomic instability

Published in Review of Development Economics, 2020

This paper examines the relationship between macroeconomic instability, as measured by the variance of inflation and output, and income inequality. We develop a panel data set consisting of 61 developed and developing economies for 1990–2019. Our results highlight a positive relationship between past inflation variance and subsequent inequality. We find that this relationship is nonexistent in developed countries but is strong for developing economies. Developing countries that have adopted an inflation targeting (IT) regime are insulated from the regressive effect of inflation volatility. From the point of view of achieving an even income distribution, IT may be consistent with better equity and efficiency outcomes simultaneously. The main findings are robust to a rich set of controls, alternative measures of volatility and inequality, various subsample checks, and dynamic panel specifications.

Recommended citation: Memon, S., & Qureshi, I. A. (2021). Income inequality and macroeconomic instability. Review of Development Economics, 25(2), 758-789.
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