Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/44087
Title: Forecasting with model selection or model averaging : a case study for monthly container port throughput
Authors: Gao, Y
Luo, M 
Zou, G
Keywords: Container throughput
Model combination
Model selection
Structural change VAR model
Time series forecast
Issue Date: 2016
Publisher: Taylor & Francis
Source: Transportmetrica. A, Transport science, 2016, v. 12, no. 4, p. 366-384 How to cite?
Journal: Transportmetrica. A, Transport science 
Abstract: An accurate short-term prediction of time series data is critical to operational decision-making. While most forecasts are made based on one selected model according to certain criteria, there are developments that harness the advantages of different models by combining them together in the prediction process. Following on from existing work, this paper applies six model selection criteria and six model averaging (MA) criteria to a structural change vector Autoregressive model, and compares them in terms of both the theoretical background and empirical results. A case study of the monthly container port throughput forecasting for two competing ports shows that, in general, the model averaging methods perform better than the model selection methods. In particular, the leave-subject-out cross-validation MA method is the best in the sense of achieving the lowest average of mean-squared forecast errors.
URI: http://hdl.handle.net/10397/44087
ISSN: 2324-9935
EISSN: 2324-9943
DOI: 10.1080/23249935.2015.1137652
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