4 papers
Principal Component Analysis for High-Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification
Matteo Barigozzi
We consider estimation of large approximate factor models in high-dimensional panels of stationary time series using Principal Component Analysis (PCA). We review the key results e…
Factor Network Autoregressions
Matteo Barigozzi, Giuseppe Cavaliere, Graziano Moramarco
We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections b…
FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time series
Matteo Barigozzi, Haeran Cho, Dom Owens
We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under…
Statistical inference for large-dimensional tensor factor model by iterative projections
Matteo Barigozzi, Yong He, Lingxiao Li +1
Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order large-dimensional tensor time series, and have wide applications in economics, finance and medical…