Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods-Reference-Cited by-同舟云学术

Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods

Published:2016-01-19 Issue:4 Volume:51 Page:1541-1556
ISSN:0377-7332
Container-title:Empirical Economics
language:en
Short-container-title:Empir Econ

Author:

Babikir Ali,Mwambi Henry

Publisher

Springer Science and Business Media LLC

Subject

Economics and Econometrics,Social Sciences (miscellaneous),Mathematics (miscellaneous),Statistics and Probability

Link

http://link.springer.com/content/pdf/10.1007/s00181-015-1049-1.pdf

Reference42 articles.

1. Aladag CH, Egrioglu E, Yolcu U (2010) Forecast combination by using artificial neural networks. Neural Process Lett 32:269–276

2. Alessi L, Barigozzi M, Capasso M (2008) A robust criterion for determining the number of static factors in approximate factor models. Working paper no. 903, European Central Bank

3. Amengual D, Watson MW (2007) Consistent estimation of the number of dynamic factors in a large N and T panel. J Bus Econ Stat 25:91–96

4. Artis MJ, Banerjee A, Marcellino M (2005) Factor forecasts for the UK. J Forecast 24(4):279–298

5. Bai J, Ng S (2002) Determining the number of factors in approximate factor models. Econometrica 70(1):191–221

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Forecast combination using grey relational analysis and Choquet fuzzy integral for container throughput forecasting;Expert Systems with Applications;2024-10

2. Forecast combination in agricultural economics: Past, present, and the future;Applied Economic Perspectives and Policy;2024-05-16

3. Stacking for Probabilistic Short-Term Load Forecasting;Lecture Notes in Computer Science;2024

4. Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality;2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA);2023-10-09

5. Combining Forecasts of Time Series with Complex Seasonality Using LSTM-Based Meta-Learning;ITISE 2023;2023-07-05