**Dynamic Factor Model and Artificial Neural Network Models: To Combine Forecasts or Combine Models?** Dynamic Factor Model and Artificial Neural Network Models: To Combine Forecasts or Combine Models?

DOI: 10.5772/intechopen.71536

Ali Babikir, Mustafa Mohammed and Henry Mwambi Ali Babikir, Mustafa Mohammed and

Additional information is available at the end of the chapter Henry Mwambi

http://dx.doi.org/10.5772/intechopen.71536 Additional information is available at the end of the chapter

#### Abstract

In this chapter, we evaluate the forecasting performance of the model combination and forecast combination of the dynamic factor model (DFM) and the artificial neural networks (ANNs). For the model combination, the factors that are extracted from a large dataset are used as additional input to the ANN model that produces the factoraugmented artificial neural network (FAANN). Linear and nonlinear forecasts combining methods are used to combine the DFM and the ANN forecasts. The results of the best combining method are compared to the forecasts result of the FAANN model. The models are applied to forecast three time series variables using large South African monthly data. The out-of-sample root-mean-square error (RMSE) results show that the FAANN model yields substantial improvement over the individual and best combined forecasts from the DFM and ANN forecasting models and the autoregressive AR benchmark model. Further, the Diebold-Mariano test results also confirm the superiority of the FAANN model forecast's performance over the AR benchmark model and the combined forecasts.

Keywords: artificial neural network, dynamic factor model, factor-augmented artificial neural network model, forecasts combination, forecasting
