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		Codes

Determining the number of static factors in approximate factor models       Matlab zip file Reference: Improved penalization for determining the number of factors in approximate static factor models, L. Alessi, M. Barigozzi, M. Capasso, Statistics and Probability Letters, 2010. QML estimation of dynamic factor models       Matlab zip file Reference: Quasi maximum likelihood estimation and inference of large approximate dynamic factor models via the EM algorithm, M. Barigozzi, M. Luciani, arXiv, 2022. A quasi-maximum likelihood approach for large approximate dynamic factor models, C. Doz, D. Giannone, L. Reichlin, The Review of Economics and Statistics, 2012. Generalized dynamic factor model       Matlab zip file References: The Generalized Dynamic Factor Model: identification and estimation, M. Forni, M. Hallin, M. Lippi, L. Reichlin, The Review of Economics and Statistics, 2000. The Generalized Dynamic Factor Model: one-sided estimation and forecasting, M. Forni, M. Hallin, M. Lippi, L. Reichlin, Journal of the American Statistical Association, 2005. Dynamic Factor Models with infinite-dimensional factor space: asymptotic analysis, M. Forni, M. Hallin, M. Lippi, P. Zaffaroni, Journal of Econometrics, 2017. Determining the number of factors in the general dynamic factor model, M. Hallin, R. Liška, Journal of the American Statistical Association, 2007. fnets       R package References: FNETS: factor-adjusted network estimation and forecasting for high-dimensional time series, M. Barigozzi, H. Cho, D. Owens, Journal of Economics & Business Statistics, 2023. fnets: an R package for network estimation and forecasting via factor-adjusted VAR modelling, D. Owens, H. Cho, M. Barigozzi, The R Journal, 2023. nets       R package Reference: NETS: network estimation for time series, M. Barigozzi, C. Brownlees, Journal of Applied Econometrics, 2019. rtfa       R package References: Robust tensor factor analysis, M. Barigozzi, Y. He, L. Li, L. Trapani, arXiv, 2023. factorcpt       R package Reference: Simultaneous multiple change–point and factor analysis for high-dimensional time series, M. Barigozzi, H. Cho, P. Fryzlewicz, Journal of Econometrics, 2018. BTtest       R package Reference: Testing for common trends in non-stationary large datasets, M. Barigozzi, L. Trapani, Journal of Business & Economic Statistics, 2022.