J. Bai, Estimating cross-section common stochastic trends in nonstationary panel data, Journal of Econometrics, vol.122, issue.1, pp.137-183, 2004.
DOI : 10.1016/j.jeconom.2003.10.022

J. Bai, Inferential Theory for Factor Models of Large Dimensions, Econometrica, vol.71, issue.1, pp.135-171, 2003.
DOI : 10.1111/1468-0262.00392

J. Bai and K. Li, Maximum Likelihood Estimation and Inference for Approximate Factor Models of High Dimension, Review of Economics and Statistics, vol.31, issue.2, pp.98-298, 2016.
DOI : 10.1214/aos/1176346060

URL : https://mpra.ub.uni-muenchen.de/42099/1/MPRA_paper_42099.pdf

J. Bai and S. Ng, Determining the Number of Factors in Approximate Factor Models, Econometrica, vol.70, issue.1, pp.191-221, 2002.
DOI : 10.1111/1468-0262.00273

J. Bai and S. Ng, A PANIC Attack on Unit Roots and Cointegration, Econometrica, vol.72, issue.4, pp.1127-1177, 2004.
DOI : 10.1111/j.1468-0262.2004.00528.x

URL : http://fmwww.bc.edu/EC-P/WP519.pdf

J. Bai and S. Ng, Determining the Number of Primitive Shocks in Factor Models, Journal of Business & Economic Statistics, vol.25, issue.1, pp.52-60, 2007.
DOI : 10.1198/073500106000000413

URL : http://www.columbia.edu/~sn2294/pub/jbes07.pdf

J. Bai and S. Ng, Large Dimensional Factor Analysis, Foundations and Trends?? in Econometrics, vol.3, issue.2, pp.89-163, 2008.
DOI : 10.1561/0800000002

URL : https://www.nowpublishers.com/article/DownloadSummary/ECO-002

A. Banerjee and M. Marcellino, Factor-augmented Error Correction Models Eds The methodology and practice of econometrics -a festschrift for David Hendry, pp.227-254, 2009.

A. Banerjee, M. Marcellino, and I. Masten, Forecasting with factor-augmented error correction models, International Journal of Forecasting, vol.30, issue.3, pp.589-612, 2014.
DOI : 10.1016/j.ijforecast.2013.01.009

URL : http://nber-nsf09.ucdavis.edu/program/papers/marcellino.pdf

M. Barigozzi, M. Lippi, and M. Luciani, Dynamic factor models, cointegration, and error correction mechanisms, 2017.
DOI : 10.17016/feds.2016.018

URL : https://doi.org/10.17016/feds.2016.018

M. Barigozzi, M. Lippi, and M. Luciani, Non-Stationary Dynamic Factor Modelsfor Large Datasets, 2017.
DOI : 10.17016/feds.2016.024r1

URL : https://doi.org/10.17016/feds.2016.024r1

B. S. Bernanke, J. Boivin, and P. Eliasz, Measuring The Eects Of Monetary Policy : A Factor- Augmented Vector Autoregressive (FAVAR) Approach, Quarterly Journal of Economics, vol.120, issue.1, pp.387-422, 2005.
DOI : 10.3386/w10220

M. Bessec and C. Doz, Pr??vision ?? court terme de la croissance du PIB fran??ais ?? l???aide de mod??les ?? facteurs dynamiques, ??conomie & pr??vision, vol.199, issue.1, pp.1-30, 2012.
DOI : 10.3406/ecop.2012.8096

URL : http://www.persee.fr/docAsPDF/ecop_0249-4744_2012_num_199_1_8096.pdf

C. Doz, D. Giannone, and L. Reichlin, A two-step estimator for large approximate dynamic factor models based on Kalman filtering, Journal of Econometrics, vol.164, issue.1, pp.188-205, 2011.
DOI : 10.1016/j.jeconom.2011.02.012

URL : https://hal.archives-ouvertes.fr/hal-00844811

J. Durbin, S. J. Et, and . Koopman, Time Series Analysis by State Space Methods, 2001.
DOI : 10.1093/acprof:oso/9780199641178.001.0001

R. F. Engle and C. W. Et-granger, Co-Integration and Error Correction: Representation, Estimation, and Testing, Econometrica, vol.55, issue.2, pp.251-276, 1987.
DOI : 10.2307/1913236

A. C. Harvey, Forecasting, structural time series models and the Kalman lter, 1991.

S. Johansen, Likelihood-based inference in cointegrated vector-autoregressive models, 1995.
DOI : 10.1093/0198774508.001.0001

A. Onatski, Determining the Number of Factors from Empirical Distribution of Eigenvalues, Review of Economics and Statistics, vol.36, issue.9, pp.1004-1016, 2010.
DOI : 10.1198/073500106000000585

J. H. Stock and M. W. Watson, Testing for Common Trends, Journal of the American Statistical Association, vol.83, issue.404, pp.1097-1107, 1988.
DOI : 10.1214/aoms/1177706450

J. H. Stock and M. W. Watson, Forecasting Using Principal Components From a Large Number of Predictors, Journal of the American Statistical Association, vol.97, issue.460, pp.1167-1179, 2002.
DOI : 10.1198/016214502388618960

URL : http://www.princeton.edu/~mwatson/papers/Stock_Watson_JASA_2002.pdf

J. H. Stock and M. W. Watson, Macroeconomic Forecasting Using Diffusion Indexes, Journal of Business & Economic Statistics, vol.20, issue.2, pp.147-162, 2002.
DOI : 10.1198/073500102317351921

URL : http://scholar.harvard.edu/files/stock/files/macroeconomic_forecasting_using_diffusion_indexes.pdf

J. H. Stock and M. W. Watson, Dynamic Factor Models",in Clements MP, Henry DF Oxford Handbook of Economic Forecasting, quarterly; M = monthly. IPI (C1) (or C2, C3, C5, in that order) represents the industrial production index in the following activities of the aggregated classication, 2010.

, C1 = Manufacture of food products and beverages, C2 = Manufacture of coke and rened petroleum products, C3 = Electrical and electronic equipment; machine equipment, C5 = other manufacturing (excluding transport)

, CL2 = Manufacture of other transport equipment Sources : a) INSEE; b) Eurostat; c) OECD; d) French Ministry of Ecology; e) Nyse Euronext Paris; f) Standard & Poor's; g) FTSE; h) Frankfurt SE; i) Financial Times; j) OSE; k) CBOE; l) Bank of France; m) IMF; n) Daily press; o) Global insight; p) LBMA; q) ECB; r) IFO; s) ZEW; t) DESTATIS; u) Census Bureau; v) Federal Reserve Board, CL1 = Manufacture of motor vehicles, trailers, and semi-trailers BLS; x) ISM. Timeliness : M+k for a series which is available k months after the end of month M