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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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    Cited by:

    1. Cimadomo, Jacopo & Bénassy-Quéré, Agnès, 2012. "Changing patterns of fiscal policy multipliers in Germany, the UK and the US," Journal of Macroeconomics, Elsevier, vol. 34(3), pages 845-873.
    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. 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.
    4. Thomas Helbling & 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 03/46, International Monetary Fund.
    5. Marlene Amstad & Andreas Fischer, 2005. "Shock Identification of Macroeconomic Forecasts based on Daily Panels," Working Papers 05.02, Swiss National Bank, Study Center Gerzensee.
    6. 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.
    7. 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.
    8. 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.

    More about this item

    Keywords

    business cycles; factor analysis; panel data;

    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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