TY  - CHAP
AB  - Abstract Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. This paper proposes to use prediction weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to short-term forecasts of euro area, German, and French GDP growth from unbalanced monthly data suggest that both prediction weights and least angle regressions result in improved nowcasts. Overall, prediction weights provide yet more robust results.
VL  - 35
SN  - 978-1-78560-353-2, 978-1-78560-352-5/0731-9053
DO  - 10.1108/S0731-905320150000035016
UR  - https://doi.org/10.1108/S0731-905320150000035016
AU  - Rünstler Gerhard
PY  - 2016
Y1  - 2016/01/01
TI  - On the Design of Data Sets for Forecasting with Dynamic Factor Models
T2  - Dynamic Factor Models
T3  - Advances in Econometrics
PB  - Emerald Group Publishing Limited
SP  - 629
EP  - 662
Y2  - 2024/05/13
ER  -