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Machine Learning for Economists: An Introduction

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  • Sonan Memon

    (Pakistan Institute of Development Economics)

Abstract

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.[1] 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.

Suggested Citation

  • Sonan Memon, 2021. "Machine Learning for Economists: An Introduction," PIDE Knowledge Brief 2021:33, Pakistan Institute of Development Economics.
    • Handle: RePEc:pid:kbrief:2021:33
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    References listed on IDEAS

    as
    1. Larsen, Vegard H. & Thorsrud, Leif Anders & Zhulanova, Julia, 2021. "News-driven inflation expectations and information rigidities," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 507-520.
    2. Stephen Hansen & Michael McMahon, 2016. "Shocking Language: Understanding the Macroeconomic Effects of Central Bank Communication," NBER Chapters, in: NBER International Seminar on Macroeconomics 2015, National Bureau of Economic Research, Inc.
    3. Matthew Gentzkow & Bryan Kelly & Matt Taddy, 2019. "Text as Data," Journal of Economic Literature, American Economic Association, vol. 57(3), pages 535-574, September.
    4. A. Stefano Caria & Grant Gordon & Maximilian Kasy & Simon Quinn & Soha Shami & Alexander Teytelboym, 2020. "An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan," CSAE Working Paper Series 2020-20, Centre for the Study of African Economies, University of Oxford.
    5. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    6. Athey, Susan & Imbens, Guido W., 2015. "Machine Learning for Estimating Heterogeneous Causal Effects," Research Papers 3350, Stanford University, Graduate School of Business.
    7. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    8. Anders Bredahl Kock & Laurent A.F. Callot, 2012. "Oracle Efficient Estimation and Forecasting with the Adaptive LASSO and the Adaptive Group LASSO in Vector Autoregressions," CREATES Research Papers 2012-38, Department of Economics and Business Economics, Aarhus University.
    9. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
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    Keywords

    Machine Learning; Economists; Introduction;
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