In this event, the keynote speaker presents a computationally simple estimator of the asymptotic covariance matrix of the Principal Components (PC) factors valid in the presence of cross-correlated idiosyncratic components. The proposed estimator of the asymptotic Mean Square Error (MSE) of PC factors is based on adaptive thresholding the sample covariances of the idiosyncratic residuals with the threshold based on their individual variances. The research compares the finite sample performance of confidence regions for the PC factors obtained using the proposed asymptotic MSE with those of available extant asymptotic and bootstrap regions and show that the former beats all alternative procedures for a wide variety of idiosyncratic cross-correlation structures.

Keynote speaker
Esther Ruiz Ortega is Full Professor of Econometrics at Universidad Carlos III de Madrid. She holds a PhD from the London School of Economics and is one of the Senior Editors of the International Journal of Forecasting. Her research interests fall along the intersection between time series econometrics, financial econometrics, economic forecasting and large multivariate time series models. She has published numerous articles in very prestigious journals and is, in September 2024, among the top 5% female economists in the database RePEc/IDEAS.