| Full text | |
| Author(s): |
Trucios, Carlos
[1]
;
Mazzeu, Joao H. G.
[2]
;
Hotta, Luiz K.
[2]
;
Valls Pereira, Pedro L.
[1]
;
Hallin, Marc
[3]
Total Authors: 5
|
| Affiliation: | [1] Sao Paulo Sch Econ, FGV, Sao Paulo, SP - Brazil
[2] Univ Estadual Campinas, Dept Stat, Campinas, SP - Brazil
[3] Univ Libre Bruxelles, ECARES, Brussels - Belgium
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF FORECASTING; v. 37, n. 4, p. 1520-1534, OCT-DEC 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in the analysis of high-dimensional time series and have been successfully considered in many economic and financial applications. As second-order models, however, they are sensitive to the presence of outliers-an issue that has not been analyzed so far in the general case of dynamic factors with possibly infinite-dimensional factor spaces (Forni et al. 2000, 2015, 2017). In this paper, we consider this robustness issue and study the impact of additive outliers on the identification, estimation, and forecasting performance of general dynamic factor models. Based on our findings, we propose robust versions of identification, estimation, and forecasting procedures. The finite-sample performance of our methods is evaluated via Monte Carlo experiments and successfully applied to a classical data set of 115 US macroeconomic and financial time series. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 16/18599-4 - Modeling and forecasting volatility of high dimensional financial series |
| Grantee: | Carlos Cesar Trucios Maza |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 18/03012-3 - Robust dynamic dimension reduction techniques for volatilities |
| Grantee: | Carlos Cesar Trucios Maza |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |
| FAPESP's process: | 18/04654-9 - Time series, wavelets and high dimensional data |
| Grantee: | Pedro Alberto Morettin |
| Support Opportunities: | Research Projects - Thematic Grants |