<abstract xmlns="http://www.w3.org/1999/xhtml"><p> We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson (2002a, b). The second is based on generalized principal components and it was introduced by Forni, Hallin, Lippi and Reichlin (2000, 2005). The last model has been recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015, 2016). The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a in- sample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to Forni, Giovannelli, Lippi and Soccorsi (2016), but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.</p></abstract>

We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson (2002a, b). The second is based on generalized principal components and it was introduced by Forni, Hallin, Lippi and Reichlin (2000, 2005). The last model has been recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015, 2016). The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a in- sample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to Forni, Giovannelli, Lippi and Soccorsi (2016), but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.

A forecasting performance comparison of dynamic factor models based on static and dynamic methods

Communications in Applied and Industrial Mathematics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series...

A forecasting performance comparison of dynamic factor models based on static and dynamic methods

Data publikacji: 22 mar 2017

Zakres stron: 43 - 66

Otrzymano: 14 gru 2016

Przyjęty: 16 lut 2017

DOI: https://doi.org/10.1515/caim-2017-0003

Słowa kluczowe
Macroeconomic Forecasting, Dynamic Factor Models, Time domain methods, Frequency domain methods

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A forecasting performance comparison of dynamic factor models based on static and dynamic methods

Data publikacji: 22 mar 2017

Zakres stron: 43 - 66

Otrzymano: 14 gru 2016

Przyjęty: 16 lut 2017

DOI: https://doi.org/10.1515/caim-2017-0003

Słowa kluczoweMacroeconomic Forecasting, Dynamic Factor Models, Time domain methods, Frequency domain methods

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Macroeconomic Forecasting, Dynamic Factor Models, Time domain methods, Frequency domain methods