Home       Bio       Publications       Working papers       EA dataset       Codes       Lecture notes

		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, 2024. 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. Monetary policy shocks in the euro area - part 1 - 2014       Matlab Monetary policy shocks in the euro area - part 2 - 2024       Matlab References: Do euro area countries respond asymmetrically to the common monetary policy?, M. Barigozzi, A. Conti, M. Luciani, Oxford Bulletin of Economics and Statistics, 2014. Large datasets for the euro area and its member countries and the dynamic effects of the common monetary policy, M. Barigozzi, C. Lissona, L. Tonni, 2024 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, 2024. 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.