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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 quasimaximum 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: onesided estimation and forecasting, M. Forni, M. Hallin, M. Lippi, L. Reichlin, Journal of the American Statistical Association, 2005. Dynamic Factor Models with infinitedimensional 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: factoradjusted network estimation and forecasting for highdimensional time series, M. Barigozzi, H. Cho, D. Owens, Journal of Economics & Business Statistics, 2023. fnets: an R package for network estimation and forecasting via factoradjusted 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 highdimensional time series, M. Barigozzi, H. Cho, P. Fryzlewicz, Journal of Econometrics, 2018. BTtest R package Reference: Testing for common trends in nonstationary large datasets, M. Barigozzi, L. Trapani, Journal of Business & Economic Statistics, 2022. 