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Pena, D. and Rodriguez, J. (2005) Detecting Nonlinearity in Time Series by Model Selection Criteria. International Journal of Forecasting, 21, 731-748.
https://doi.org/10.1016/j.ijforecast.2005.04.014

has been cited by the following article:

  • TITLE: Selection of Heteroscedastic Models: A Time Series Forecasting Approach

    AUTHORS: Imoh Udo Moffat, Emmanuel Alphonsus Akpan

    KEYWORDS: ARIMA Model, GARCH-Type Model, Heteroscedasticity, Model Selection, Time Series Forecasting, Volatility

    JOURNAL NAME: Applied Mathematics, Vol.10 No.5, May 23, 2019

    ABSTRACT: To overcome the weaknesses of in-sample model selection, this study adopted out-of-sample model selection approach for selecting models with improved forecasting accuracies and performances. Daily closing share prices were obtained from Diamond Bank and Fidelity Bank as listed in the Nigerian Stock Exchange spanning from January 3, 2006 to December 30, 2016. Thus, a total of 2713 observations were explored and were divided into two portions. The first which ranged from January 3, 2006 to November 24, 2016, comprising 2690 observations, was used for model formulation. The second portion which ranged from November 25, 2016 to December 30, 2016, consisting of 23 observations, was used for out-of-sample forecasting performance evaluation. Combined linear (ARIMA) and Nonlinear (GARCH-type) models were applied on the returns series with respect to normal and student-t distributions. The findings revealed that ARIMA (2,1,1)-EGARCH (1,1)-norm and ARIMA (1,1,0)-EGARCH (1,1)-norm models selected based on minimum predictive errors throughout-of-sample approach outperformed ARIMA (2,1,1)-GARCH (2,0)-std and ARIMA (1,1,0)-EGARCH (1,1)-std model chosen through in-sample approach. Therefore, it could be deduced that out-of-sample model selection approach was suitable for selecting models with improved forecasting accuracies and performances.