Prof. Matteo Barigozzi, Università di Bologna
Chair: Prof. Mattia Guerini, University of Brescia
When: Tuesday, May 20th, 2025, 2 PM - 4 PM
Where: Room A6, C.da S. Chiara 50
50We study estimation of large Dynamic Factor models implemented through the ExpectationMaximization (EM) algorithm, jointly with the Kalman smoother. We prove that as both thecross-sectional dimension, n, and the sample size, T , diverge to infinity: (i) the estimatedloadings are √T-consistent, asymptotically normal and equivalent to their Quasi MaximumLikelihood estimates; (ii) the estimated factors are √n-consistent, asymptotically normal andequivalent to their Weighted Least Squares estimates. Moreover, the estimated loadings areasymptotically as efficient as those obtained by Principal Components analysis, while theestimated factors are more efficient if the idiosyncratic covariance is sparse enough.