IDEAS home Printed from https://ideas.repec.org/p/imf/imfwpa/2016-056.html
   My bibliography  Save this paper

Seeing in the Dark: A Machine-Learning Approach to Nowcasting in Lebanon

Author

Listed:
  • Mr. Andrew J Tiffin

Abstract

Macroeconomic analysis in Lebanon presents a distinct challenge. For example, long delays in the publication of GDP data mean that our analysis often relies on proxy variables, and resembles an extended version of the “nowcasting” challenge familiar to many central banks. Addressing this problem—and mindful of the pitfalls of extracting information from a large number of correlated proxies—we explore some recent techniques from the machine learning literature. We focus on two popular techniques (Elastic Net regression and Random Forests) and provide an estimation procedure that is intuitively familiar and well suited to the challenging features of Lebanon’s data.

Suggested Citation

  • 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.
    • Handle: RePEc:imf:imfwpa:2016/056
    as

    Download full text from publisher

    File URL: http://www.imf.org/external/pubs/cat/longres.aspx?sk=43779
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    2. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    3. Matta, Samer, 2014. "New coincident and leading indicators for the Lebanese economy," Policy Research Working Paper Series 6950, The World Bank.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    2. Stankevich, Ivan, 2020. "Comparison of macroeconomic indicators nowcasting methods: Russian GDP case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 113-127.
    3. Botero García, Jesús Alonso & Hurtado, Alvaro & Montañez Herrera, Diego Fernando, 2021. "The productivity of the agricultural sector and its effects on economic growth: a CGE analysis," Conference papers 333318, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    4. Hamdy Ahmad Aly Alhendawy & Mohammed Galal Abdallah Mostafa & Mohamed Ibrahim Elgohari & Ibrahim Abdalla Abdelraouf Mohamed & Nabil Medhat Arafat Mahmoud & Mohamed Ahmed Mohamed Mater, 2023. "Determinants of Renewable Energy Production in Egypt New Approach: Machine Learning Algorithms," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 679-689, November.
    5. Juan Tenorio & Wilder Pérez, 2023. "GDP nowcasting with Machine Learning and Unstructured Data to Peru," Working Papers 197, Peruvian Economic Association.
    6. Shafiullah Qureshi & Ba Chu & Fanny S. Demers, 2021. "Forecasting Canadian GDP Growth with Machine Learning," Carleton Economic Papers 21-05, Carleton University, Department of Economics.
    7. Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
    8. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    9. Mr. Andrew J Tiffin, 2019. "Machine Learning and Causality: The Impact of Financial Crises on Growth," IMF Working Papers 2019/228, International Monetary Fund.
    10. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
    11. Maas, Benedikt, 2019. "Nowcasting and forecasting US recessions: Evidence from the Super Learner," MPRA Paper 96408, University Library of Munich, Germany.
    12. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    13. 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.
    14. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    15. Lisa-Cheree Martin, 2019. "Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP," Working Papers 12/2019, Stellenbosch University, Department of Economics.
    16. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
    17. Carlos León & Fabio Ortega, 2018. "Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach," Revista de Economía del Rosario, Universidad del Rosario, vol. 21(2), pages 381-407, December.
    18. 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.
    19. Juan Tenorio & Wilder Perez, 2024. "Monthly GDP nowcasting with Machine Learning and Unstructured Data," Papers 2402.04165, arXiv.org.
    20. 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.
    21. Marijn A. Bolhuis & Brett Rayner, 2020. "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers 2020/045, International Monetary Fund.
    22. McSharry, Patrick & Mawejje, Joseph, 2024. "Estimating urban GDP growth using nighttime lights and machine learning techniques in data poor environments: The case of South Sudan," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    23. Klaus-Peter Hellwig, 2018. "Overfitting in Judgment-based Economic Forecasts: The Case of IMF Growth Projections," IMF Working Papers 2018/260, International Monetary Fund.

    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. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    2. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    3. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
    4. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
    5. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    6. Nicodemo, Catia & Satorra, Albert, 2020. "Exploratory Data Analysis on Large Data Sets: The Example of Salary Variation in Spanish Social Security Data," IZA Discussion Papers 13459, Institute of Labor Economics (IZA).
    7. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    8. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    9. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    10. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.
    11. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    12. Leif Anders Thorsrud, 2016. "Nowcasting using news topics Big Data versus big bank," Working Papers No 6/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    13. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
    14. Lopez Cordova,Jose Ernesto, 2020. "Digital Platforms and the Demand for International Tourism Services," Policy Research Working Paper Series 9147, The World Bank.
    15. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    16. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    17. Jens Ludwig & Sendhil Mullainathan, 2021. "Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System," Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 71-96, Fall.
    18. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
    19. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
    20. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.

    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:imf:imfwpa:2016/056. 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: Akshay Modi (email available below). General contact details of provider: https://edirc.repec.org/data/imfffus.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.