IDEAS home Printed from https://ideas.repec.org/p/awe/wpaper/446.html

New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning

Author

Listed:
  • Sabyasachi Kar
  • Amaani Bashir
  • Mayank Jain

    (Institute of Economic Growth, Delhi)

Abstract

The use of big data and machine learning techniques is now very common in many spheres and there is growing popularity of these approaches in macroeconomic forecasting as well. Is big data and machine learning really useful in the prediction of macroeconomic outcomes? Are they superior in performance compared to their traditional counterparts? What are the tradeoffs that forecasters need to keep in mind, and what are the steps they need to take to use these resources effectively? We carry out a critical analysis of the existing literature in order to answer these questions. Our analysis suggests that the answer to most of these questions are nuanced, conditional on a number of factors identified in the study.

Suggested Citation

  • Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021. "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers 446, Institute of Economic Growth.
    • Handle: RePEc:awe:wpaper:446
    as

    Download full text from publisher

    File URL: https://iegindia.org/upload/publication/Workpap/wp446.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jahn, Malte, 2018. "Artificial neural network regression models: Predicting GDP growth," HWWI Research Papers 185, Hamburg Institute of International Economics (HWWI).
    2. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    3. Mr. Andrew J Tiffin, 2016. "Seeing in the Dark: A Machine-Learning Approach to Nowcasting in Lebanon," IMF Working Papers 2016/056, International Monetary Fund.
    4. Jin-Kyu Jung & Manasa Patnam & Anna Ter-Martirosyan, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
    5. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    6. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    7. John B. Taylor, 1999. "Introduction to "Monetary Policy Rules"," NBER Chapters, in: Monetary Policy Rules, pages 1-14, National Bureau of Economic Research, Inc.
    8. Jurgen A. Doornik & David F. Hendry & Steve Cook, 2015. "Statistical model selection with “Big Data”," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1045216-104, December.
    9. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    10. Nicolai Meinshausen & Peter Bühlmann, 2010. "Stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 417-473, September.
    11. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    12. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
    13. Schnaubelt, Matthias, 2019. "A comparison of machine learning model validation schemes for non-stationary time series data," FAU Discussion Papers in Economics 11/2019, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    14. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
    15. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
    16. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    17. John B. Taylor, 1999. "A Historical Analysis of Monetary Policy Rules," NBER Chapters, in: Monetary Policy Rules, pages 319-348, National Bureau of Economic Research, Inc.
    18. John B. Taylor, 1999. "Monetary Policy Rules," NBER Books, National Bureau of Economic Research, Inc, number tayl99-1, September.
    19. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ireland, Peter N., 2003. "Endogenous money or sticky prices?," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1623-1648, November.
    2. Coenen, Gunter & Wieland, Volker, 2003. "The zero-interest-rate bound and the role of the exchange rate for monetary policy in Japan," Journal of Monetary Economics, Elsevier, vol. 50(5), pages 1071-1101, July.
    3. Wollmershauser, Timo, 2006. "Should central banks react to exchange rate movements? An analysis of the robustness of simple policy rules under exchange rate uncertainty," Journal of Macroeconomics, Elsevier, vol. 28(3), pages 493-519, September.
    4. Coenen, Gunter & Wieland, Volker, 2005. "A small estimated euro area model with rational expectations and nominal rigidities," European Economic Review, Elsevier, vol. 49(5), pages 1081-1104, July.
    5. Marijn A. Bolhuis & Brett Rayner, 2020. "The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data," IMF Working Papers 2020/044, International Monetary Fund.
    6. Nelson, Edward, 2003. "The future of monetary aggregates in monetary policy analysis," Journal of Monetary Economics, Elsevier, vol. 50(5), pages 1029-1059, July.
    7. Galí, Jordi, 2010. "Monetary Policy and Unemployment," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 10, pages 487-546, Elsevier.
    8. Eric Swanson, 2015. "A Macroeconomic Model of Equities and Real, Nominal, and Defaultable Debt," 2015 Meeting Papers 273, Society for Economic Dynamics.
    9. Marco Del Negro & Frank Schorfheide, 2009. "Monetary Policy Analysis with Potentially Misspecified Models," American Economic Review, American Economic Association, vol. 99(4), pages 1415-1450, September.
    10. Adnan Haider & Musleh ud Din & Ejaz Ghani, 2012. "Monetary Policy, Informality and Business Cycle Fluctuations in a Developing Economy Vulnerable to External Shocks," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 51(4), pages 609-681.
    11. Gustavo Bussinger, 2004. "El canal de crédito como mecanismo de transmisión de la política monetaria en Brasil," Monetaria, CEMLA, vol. 0(3), pages 243-262, julio-sep.
    12. Paul Ho & Pierre-Daniel G. Sarte & Felipe Schwartzman, 2022. "Multilateral Comovement in a New Keynesian World: A Little Trade Goes a Long Way," Working Paper 22-10, Federal Reserve Bank of Richmond.
    13. Flaschel, Peter & Gong, Gang & Semmler, Willi, 2001. "A Keynesian macroeconometric framework for the analysis of monetary policy rules," Journal of Economic Behavior & Organization, Elsevier, vol. 46(1), pages 101-136, September.
    14. Enrico Sergio Levrero, 2022. "The Taylor Rule and its Aftermath: Elements for an Interpretation along Classical-Keynesian lines," Centro Sraffa Working Papers CSWP59, Centro di Ricerche e Documentazione 'Piero Sraffa'.
    15. Meyer, Laurence H., 2002. "Inflation targets and inflation targeting," The North American Journal of Economics and Finance, Elsevier, vol. 13(2), pages 147-162, August.
    16. Deming Luo & Stephen Ferris, 2008. "Optimal Simple Monetary Policy Rules in a Small Open Economy with Exchange Rate Imperfections," Carleton Economic Papers 08-03, Carleton University, Department of Economics.
    17. Chetan Ghate & Sargam Gupta & Debdulal Mallick, 2018. "Terms of Trade Shocks and Monetary Policy in India," Computational Economics, Springer;Society for Computational Economics, vol. 51(1), pages 75-121, January.
    18. Bullard, James & Mitra, Kaushik, 2002. "Learning about monetary policy rules," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1105-1129, September.
    19. Mehrotra, Aaron & Sánchez-Fung, José R., 2011. "Assessing McCallum and Taylor rules in a cross-section of emerging market economies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(2), pages 207-228, April.
    20. Michael D. Bauer & Eric T. Swanson, 2023. "A Reassessment of Monetary Policy Surprises and High-Frequency Identification," NBER Macroeconomics Annual, University of Chicago Press, vol. 37(1), pages 87-155.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • 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
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:awe:wpaper:446. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/iegggin.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.