D. W. Andrews, Tests for parameter instability and structural change with unknown change point, Econometrica, vol.61, issue.4, pp.821-856, 1993.

J. Bai, X. Han, and Y. Shi, forthcoming), 'Estimation and inference of change points in high dimensional factor models, Journal of Econometrics

J. Bai and S. Ng, Determining the number of factors in approximate factor models, Econometrica, vol.70, issue.1, pp.191-221, 2002.

J. Bai and S. Ng, Principal components estimation and identification of static factors, Journal of Econometrics, vol.176, issue.1, pp.18-29, 2013.

J. Bai and P. Perron, Estimating and testing linear models with multiple structural changes, Econometrica, vol.66, pp.47-78, 1998.

J. Bai and P. Perron, Computation and analysis of multiple structural change models, Journal of Applied Econometrics, vol.18, issue.1, pp.1-22, 2003.

B. H. Baltagi, C. Kao, and F. Wang, Identification and estimation of a large factor model with structural instability, Journal of Econometrics, vol.197, issue.1, pp.87-100, 2017.

M. Ba?bura and G. Rünstler, A look into the factor model black box: publication lags and the role of hard and soft data in forecasting GDP, International Journal of Forecasting, vol.27, issue.2, pp.333-346, 2011.

M. Barigozzi, H. Cho, and P. Fryzlewicz, Simultaneous multiple change-point and factor analysis for high-dimensional time series, Journal of Econometrics, vol.206, issue.1, pp.187-225, 2018.

B. J. Bates, M. Plagborg-møller, J. H. Stock, and M. W. Watson, Consistent factor estimation in dynamic factor models with structural instability, Journal of Econometrics, vol.177, issue.2, pp.289-304, 2013.

C. Bergmeir, R. J. Hyndman, and B. Koo, A note on the validity of cross-validation for evaluating autoregressive time series prediction, Computational Statistics & Data Analysis, vol.120, pp.70-83, 2018.

B. S. Bernanke, J. Boivin, and P. Eliasz, Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach, The Quarterly Journal of Economics, vol.120, issue.1, pp.387-422, 2005.

S. Beyeler and S. Kaufmann, Reduced-form factor augmented VAR -exploiting sparsity to include meaningful factors, 2019.

J. Breitung and S. Eickmeier, Testing for structural breaks in dynamic factor models, Journal of Econometrics, vol.163, issue.1, pp.71-84, 2011.

J. B. Carroll, An analytical solution for approximating simple structure in factor analysis, Psychometrika, vol.18, issue.1, pp.23-38, 1953.

R. B. Cattell, The Scientific Use of Factor Analysis in Behavioral and Life Sciences, 1978.

G. Chamberlain and M. Rothschild, Arbitrage, factor structure, and mean-variance analysis on large asset markets, Econometrica, vol.51, issue.5, pp.1305-1324, 1983.

L. Chen, J. J. Dolado, and J. Gonzalo, Detecting big structural breaks in large factor models, Journal of Econometrics, vol.180, issue.1, pp.30-48, 2014.

X. Cheng, Z. Liao, and F. Schorfheide, Shrinkage estimation of high-dimensional factor models with structural instabilities, The Review of Economic Studies, vol.83, issue.4, pp.1511-1543, 2016.

V. Corradi and N. R. Swanson, Testing for structural stability of factor augmented forecasting models, Journal of Econometrics, vol.182, issue.1, pp.100-118, 2014.

A. B. Costello and J. W. Osborne, Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis', Practical Assessment, Research & Evaluation, vol.10, issue.7, pp.1-9, 2005.

C. Croux and P. Exterkate, Sparse and robust factor modelling, Tinbergen Institute Discussion Papers, 2011.

A. Aspremont, F. Bach, and L. El-ghaoui, Optimal solutions for sparse principal component analysis, Journal of Machine Learning Research, vol.9, pp.1269-1294, 2008.

M. Del-negro and C. Otrok, Dynamic factor models with time-varying parameters: measuring changes in international business cycles, 2008.

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.
URL : https://hal.archives-ouvertes.fr/hal-00638009

C. Doz, D. Giannone, and L. Reichlin, A quasi-maximum likelihood approach for large, approximate dynamic factor models, Review of Economics and Statistics, vol.94, issue.4, pp.1014-1024, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00638440

S. Eickmeier, Comovements and heterogeneity in the euro area analyzed in a nonstationary dynamic factor model, Journal of Applied Econometrics, vol.24, issue.6, pp.933-959, 2009.

L. R. Fabrigar, D. T. Wegener, R. C. Maccallum, and E. J. Strahan, Evaluating the use of exploratory factor analysis in psychological research, Psychological Methods, vol.4, issue.3, p.272, 1999.

M. Forni, D. Giannone, M. Lippi, and L. Reichlin, Opening the black box: Structural factor models with large cross sections, Econometric Theory, vol.25, issue.5, pp.1319-1347, 2009.

D. Giannone, L. Reichlin, and D. Small, Nowcasting: The real-time informational content of macroeconomic data, Journal of Monetary Economics, vol.55, issue.4, pp.665-676, 2008.

R. S. Gürkaynak, B. Sack, and E. Swanson, Do actions speak louder than words? the response of asset prices to monetary policy actions and statements, International Journal of Central Banking, vol.1, issue.1, pp.55-93, 2005.

X. Han and A. Inoue, Tests for parameter instability in dynamic factor models, Econometric Theory, vol.31, issue.5, pp.1117-1152, 2015.

R. I. Jennrich, A simple general procedure for orthogonal rotation, Psychometrika, vol.66, issue.2, pp.289-306, 2001.

R. I. Jennrich, A simple general method for oblique rotation, Psychometrika, vol.67, issue.1, pp.7-19, 2002.

R. I. Jennrich and P. Sampson, Rotation for simple loadings, Psychometrika, vol.31, issue.3, pp.313-323, 1966.

I. T. Jolliffe, Principal Component Analysis, 2002.

I. T. Jolliffe, N. T. Trendafilov, and M. Uddin, A modified principal component technique based on the LASSO, Journal of Computational and Graphical Statistics, vol.12, issue.3, pp.531-547, 2003.

H. F. Kaiser, The varimax criterion for analytic rotation in factor analysis, Psychometrika, vol.23, issue.3, pp.187-200, 1958.

S. Kaufmann and C. Schumacher, Identifying relevant and irrelevant variables in sparse factor models, Journal of Applied Econometrics, vol.32, issue.6, pp.1123-1144, 2017.

S. Kaufmann and C. Schumacher, Bayesian estimation of sparse dynamic factor models with order-independent and ex-post mode identification, Journal of Econometrics, vol.210, issue.1, pp.116-134, 2019.

H. H. Kim and N. R. Swanson, Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods, International Journal of Forecasting, vol.34, issue.2, pp.339-354, 2018.

M. A. Kose, C. Otrok, and C. H. Whiteman, International business cycles: World, region, and country-specific factors, American Economic Review, vol.93, issue.4, pp.1216-1239, 2003.

J. T. Kristensen, Diffusion indexes with sparse loadings, Journal of Business & Economic Statistics, pp.1-18, 2017.

S. Ma and L. Su, Estimation of large dimensional factor models with an unknown number of breaks, Journal of Econometrics, vol.207, issue.1, pp.1-29, 2018.

D. Massacci, Least squares estimation of large dimensional threshold factor models, Journal of Econometrics, vol.197, issue.1, pp.101-129, 2017.

M. W. Mccracken and S. Ng, FRED-MD: A monthly database for macroeconomic research, Journal of Business & Economic Statistics, vol.34, issue.4, pp.574-589, 2016.

S. Miranda-agrippino and H. Rey, US monetary policy and the global financial cycle, 2018.

T. J. Sargent and C. A. Sims, Business cycle modeling without pretending to have too much a priori economic theory, New Methods in Business Cycle Research, vol.1, pp.145-168, 1977.

H. Schneeweiss and H. Mathes, Factor analysis and principal components, Journal of Multivariate Analysis, vol.55, issue.1, pp.105-124, 1995.

H. Shen and J. Z. Huang, Sparse principal component analysis via regularized low rank matrix approximation, Journal of Multivariate Analysis, vol.99, issue.6, pp.1015-1034, 2008.

S. Smeekes and E. Wijler, Macroeconomic forecasting using penalized regression methods, International Journal of Forecasting, vol.34, issue.3, pp.408-430, 2018.

J. H. Stock and M. Watson, Forecasting in dynamic factor models subject to structural instability', The Methodology and Practice of Econometrics. A Festschrift in Honour of David F, Hendry, vol.173, p.205, 2009.

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.

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.

J. H. Stock and M. W. Watson, Disentangling the channels of the 2007-09 recession, Brookings Papers on Economic Activity, vol.1, pp.81-135, 2012.

J. H. Stock and M. W. Watson, Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics, in 'Handbook of macroeconomics, vol.2, pp.415-525, 2016.

L. Su and X. Wang, On time-varying factor models: Estimation and testing, Journal of Econometrics, vol.198, issue.1, pp.84-101, 2017.

N. T. Trendafilov, From simple structure to sparse components: a review', Computational Statistics, vol.29, issue.3-4, pp.431-454, 2014.

M. West, Bayesian factor regression models in the "large p, small n" paradigm, Bayesian Statistics, vol.7, pp.723-732, 2003.

Y. Yamamoto and S. Tanaka, Testing for factor loading structural change under common breaks, Journal of Econometrics, vol.189, issue.1, pp.187-206, 2015.

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.67, issue.2, pp.301-320, 2005.

H. Zou, T. Hastie, and R. Tibshirani, Sparse principal component analysis, Journal of Computational and Graphical Statistics, vol.15, issue.2, pp.265-286, 2006.