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A machine learning approach to construct quarterly data on intangible investment for Eurozone

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  • Alexopoulos, Angelos
  • Varthalitis, Petros

Abstract

We develop a novel approach to construct quarterly time series data for annually measured intangible investment variables. We accomplish this by using machine learning methods to explore the relationship between these variables and key macroeconomic time series available on a quarterly frequency. The proposed approach offers some advantages over other econometric techniques. Specifically, it does not require any ex-ante assumptions for the link between the quarterly time series and their annual counterpart, while minimizing the need for computationally expensive algorithms and necessitating almost no data pre-processing. To demonstrate the usefulness of the constructed data, we present some business cycles facts for the intangible economies of Eurozone and estimate a dynamic factor model.

Suggested Citation

  • Alexopoulos, Angelos & Varthalitis, Petros, 2023. "A machine learning approach to construct quarterly data on intangible investment for Eurozone," Economics Letters, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:ecolet:v:231:y:2023:i:c:s0165176523003324
    DOI: 10.1016/j.econlet.2023.111307
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    References listed on IDEAS

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    1. M. Ayhan Kose & Christopher Otrok & Eswar Prasad, 2012. "Global Business Cycles: Convergence Or Decoupling?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(2), pages 511-538, May.
    2. Carol Corrado & Jonathan Haskel & Cecilia Jona-Lasinio & Massimiliano Iommi, 2022. "Intangible Capital and Modern Economies," Journal of Economic Perspectives, American Economic Association, vol. 36(3), pages 3-28, Summer.
    3. Berger, Tino & Everaert, Gerdie & Pozzi, Lorenzo, 2021. "Testing for international business cycles: A multilevel factor model with stochastic factor selection," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    4. Corrado, Carol & Haskel, Jonathan & Jona-Lasinio, Cecilia & Iommi, Massimiliano, 2016. "Intangible investment in the EU and US before and since the Great Recession and its contribution to productivity growth," EIB Working Papers 2016/08, European Investment Bank (EIB).
    5. Roberto S. Mariano & Yasutomo Murasawa, 2010. "A Coincident Index, Common Factors, and Monthly Real GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 27-46, February.
    6. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    7. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
    8. Fiorito, Riccardo & Kollintzas, Tryphon, 1994. "Stylized facts of business cycles in the G7 from a real business cycles perspective," European Economic Review, Elsevier, vol. 38(2), pages 235-269, February.
    9. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
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    More about this item

    Keywords

    Machine learning; Intangible investment; Factor model; Business cycles;
    All these keywords.

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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