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Factor models in large cross sections of time series

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  • Lucrezia Reichlin

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.
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  • Lucrezia Reichlin, 2003. "Factor models in large cross sections of time series," ULB Institutional Repository 2013/10179, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:ulb:ulbeco:2013/10179
    Note: Conference paper presented at: World congress of the econometric society(8)
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    2. Lasse Bork, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," CREATES Research Papers 2009-11, Department of Economics and Business Economics, Aarhus University.
    3. Ioannides, Yannis M. & Soetevent, Adriaan R., 2007. "Social networking and individual outcomes beyond the mean field case," Journal of Economic Behavior & Organization, Elsevier, vol. 64(3-4), pages 369-390.
    4. 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.
    5. Mr. Tamim Bayoumi & Mr. Thomas Helbling, 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.
    6. Marlene Amstad & Andreas Fischer, 2005. "Shock Identification of Macroeconomic Forecasts based on Daily Panels," Working Papers 05.02, Swiss National Bank, Study Center Gerzensee.
    7. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2020. "Nowcasting with large Bayesian vector autoregressions," Working Paper Series 2453, European Central Bank.
    8. 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.
    9. Maria Mercanti-Guérin, 2020. "The Improvement of Retargeting by Big Data: a Decision Support that Threatens the Brand Image?," Post-Print hal-03027981, HAL.
    10. 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.
    11. Francis X. Diebold, 2020. ""Big Data" and its Origins," Papers 2008.05835, arXiv.org, revised Jan 2021.
    12. 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.
    13. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    14. Francis X. Diebold, 2012. "A Personal Perspective on the Origin(s) and Development of “Big Data": The Phenomenon, the Term, and the Discipline, Second Version," PIER Working Paper Archive 13-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 26 Nov 2012.

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    More about this item

    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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