RT Journal Article
T1 A robust procedure to build dynamic factor models with cluster structure
A1 Alonso Fernández, Andrés Modesto
A1 Galeano San Miguel, Pedro
A1 Peña, Daniel
AB Dynamic factor models provide a useful way to model large sets of time series. These data often have heterogeneity and cluster structure and the formulation and estimation of dynamic factor models should be adapted to these features. This article presents a procedure to fit Dynamic Factor Models with Cluster Structure (DFMCS), where some of the factors are global and others group-specific, to heterogeneous data that may include multivariate additive outliers and level shifts. The procedure starts with an initial cleaning of the times series from outlying effects. Then a first estimation of the possible factors is applied to the cleaned data and these factors are used to build the common component of each series. The groups are found by studying the joint dependency of these common components. Then, additional factors are estimated by using the series in each cluster and, finally, all the factors found are classified as global or group-specific. We show in a Monte Carlo study that the procedure works well and seems to be better than other alternatives in terms of estimation of factors and loadings as well as in terms of misclassification rates for the series. An example of an electricity market is presented to illustrate the advantages of cleaning for outliers and taking into account the cluster structure for understanding and forecasting.
PB Elsevier
SN 0304-4076
YR 2020
FD 2020-05
LK https://hdl.handle.net/10016/32939
UL https://hdl.handle.net/10016/32939
LA eng
DS e-Archivo
RD 1 sept. 2024