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Factor Models in Large Cross-Sections of Time Series

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  • Reichlin, Lucrezia

Abstract

This Paper reviews recent econometric work on factor models in large cross-sections of time series. In this literature, traditional factor analysis is adapted to develop parsimonious estimation methods for high dimension time series models. The review covers problems of consistency and rates ? as the dimension of the cross-section and the time dimension become large ? identification and forecasting. We also review empirical applications on measuring and interpreting business cycles.

Suggested Citation

  • Reichlin, Lucrezia, 2002. "Factor Models in Large Cross-Sections of Time Series," CEPR Discussion Papers 3285, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:3285
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    4. Damiana Giuseppina Costanzo & Damiano Bruno Silipo & Marianna Succurro, 2013. "Over-Indebtedness And Innovation: Some Preliminary Results," Working Papers 201304, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    5. Onatski, Alexei, 2012. "Asymptotics of the principal components estimator of large factor models with weakly influential factors," Journal of Econometrics, Elsevier, vol. 168(2), pages 244-258.
    6. YANNIS M. IOANNIDES & Adriaan R. Soetevent, 2005. "Social Networking And Individual Outcomes: Individual Decisions And Market Context," Working Papers 05-16, NET Institute, revised Oct 2005.
    7. Mr. Thomas Helbling & Mr. Tamim Bayoumi, 2003. "Are they All in the Same Boat? the 2000-2001 Growth Slowdown and the G-7 Business Cycle Linkages," IMF Working Papers 2003/046, International Monetary Fund.
    8. Marlene Amstad & Andreas Fischer, 2005. "Shock Identification of Macroeconomic Forecasts based on Daily Panels," Working Papers 05.02, Swiss National Bank, Study Center Gerzensee.
    9. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    10. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022. "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 500-519.
    11. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "A Dynamic Factor Analysis of Business Cycle on Firm-Level Data," LEM Papers Series 2006/27, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    12. Maria Mercanti-Guérin, 2020. "The Improvement of Retargeting by Big Data: a Decision Support that Threatens the Brand Image?," Post-Print hal-03027981, HAL.
    13. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    14. Francis X. Diebold, 2020. ""Big Data" and its Origins," Papers 2008.05835, arXiv.org, revised Jan 2021.
    15. Daniel Grenouilleau, 2006. "The Stacked Leading Indicators Dynamic Factor Model: A Sensitivity Analysis of Forecast Accuracy using Bootstrapping," European Economy - Economic Papers 2008 - 2015 249, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
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    More about this item

    Keywords

    Factor analysis; Panel data; Business cycles;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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