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A Medium-N Approach to Macroeconomic Forecasting

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Abstract

This paper considers methods for forecasting macroeconomic time series in a framework where the number of predictors, N, is too large to apply traditional regression models but not su¢ciently large to resort to statistical inference based on double asymptotics. Our interest is motivated by a body of empirical research suggesting that popular data-rich prediction methods perform best when N ranges from 20 to 50. In order to accomplish our goal, we examine the conditions under which partial least squares and principal component regression provide consistent estimates of a stable autoregressive distributed lag model as only the number of observations, T, diverges. We show both by simulations and empirical applications that the proposed methods compare well to models that are widely used in macroeconomic forecasting.

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  • Gianluca Cubadda & Barbara Guardabascio, 2010. "A Medium-N Approach to Macroeconomic Forecasting," CEIS Research Paper 176, Tor Vergata University, CEIS, revised 09 Dec 2010.
    • Handle: RePEc:rtv:ceisrp:176
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    2. Bernardini, Emmanuela & Cubadda, Gianluca, 2015. "Macroeconomic forecasting and structural analysis through regularized reduced-rank regression," International Journal of Forecasting, Elsevier, vol. 31(3), pages 682-691.
    3. Christian Gayer & Alessandro Girardi & Andreas Reuter, 2016. "Replacing Judgment by Statistics: Constructing Consumer Confidence Indicators on the basis of Data-driven Techniques. The Case of the Euro Area," Working Papers LuissLab 16125, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    4. 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.
    5. Cubadda, Gianluca & Guardabascio, Barbara, 2019. "Representation, estimation and forecasting of the multivariate index-augmented autoregressive model," International Journal of Forecasting, Elsevier, vol. 35(1), pages 67-79.
    6. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
    7. Sonja Tilly & Giacomo Livan, 2021. "Macroeconomic forecasting with statistically validated knowledge graphs," Papers 2104.10457, arXiv.org.
    8. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2013. "A general to specific approach for constructing composite business cycle indicators," Economic Modelling, Elsevier, vol. 33(C), pages 367-374.
    9. Barbara Guardabascio & Federico Brogi & Federico Benassi, 2024. "Measuring human mobility in times of trouble: an investigation of the mobility of European populations during COVID-19 using big data," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5181-5199, December.
    10. Shikha Gupta & Nand Kumar, 2022. "Globalization Versus Slowbalization: A Perspective on the Indian Economy," Journal of South Asian Development, , vol. 17(1), pages 84-107, April.
    11. A Fronzetti Colladon & B Guardabascio & R Innarella, 2021. "Using social network and semantic analysis to analyze online travel forums and forecast tourism demand," Papers 2105.07727, arXiv.org.
    12. Barbara Guardabascio & Filippo Moauro & Luke Mosley, 2024. "Indirect estimation of the monthly transport turnover indicator in Italy," Empirical Economics, Springer, vol. 67(2), pages 531-566, August.

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