Dynamic Factor Models with Infinite-Dimensional Factor Space: Asymptotic Analysis
By M. Forni, Marc Hallin, Marco Lippi, Paolo Zaffaroni. 2017
97 citations recorded by the source corpus.
Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni, Hallin, Lippi and Reichlin (2004). Those estimators, however, rely on Brillinger's dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard (static) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the factors has finite dimension, which severely restricts the generality afforded by the GDFM. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional factor space. A Monte Carlo experiment corroborates those theoretical results and demonstrates the excellent performance of those estimators in out-of-sample forecasting.
Subfields: Computational Theory and Mathematics, Condensed Matter Physics, Finance
View Paper: https://www.semanticscholar.org/paper/16d16889155195dc887087e58d019fc05a15ddf4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Condensed Matter Physics
In Finance
Citations in the corpus
- A network analysis of the volatility of high dimensional financial series (2017, background): “L/−1HTn uTt and ZTnt =Ynt −XTnt , t =1, : : : , T: Details on each step and asymptotic properties of the estimators are given in Forni et al. (2015b).”
- Non-Stationary Dynamic Factor Models for Large Datasets (2016, methodology): “Last, our approach could be generalized to build an unrestricted Non-Stationary DMF, similar to the one proposed by Forni et al. (2017, 2015) for stationary data.”
- Statistical Inference for High-Dimensional Matrix-Variate Factor Models (2020, background): “Forni et al. (2015, 2017) proposed a full dynamic factor model for vector time series which include both covariance and auto-covariance.”
- Principal Component Analysis of High-Frequency Data (2016, unknown): “The above factor models are static, as opposed to the dynamic factor models discussed in Forni et al. (2000, 2004), Forni and Lippi (2001), and their recent extensions with infinite-dimensional factor space discussed in Forni et al. (2015, 2017), for which frequency domain PCA is applied.”