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Residual Analysis for Auto-Correlated Econometric Model
Department of Mathematics, Al Qunfudha University College, Umm Al Qura University, Al Qunfudha, KSA
- 1 Department of Mathematics, Al Qunfudha University College, Umm Al Qura University, Al Qunfudha, KSA
Open Journal of Statistics·Volume 09 (2019)·Pages 48–61·Published 18 January 2019·DOI10.4236/ojs.2019.91005
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Abstract
The aim of this article is to provide residual analysis for a time series data of Gross Domestic Product (GDP) of the Sudan. An econometric time series model with macroeconomic variables is conducted to examine the goodness of fit using residual. Many statistical tests are used in time series models in order to make it a stationary series. After applying these tests, the time series became stationary and integrated; thus, Box-Jenkins procedure is used for the determination of ARIMA, AR (0,1,0) in this study. This identified technique is useful for analyzing this study.
KeywordsARIMA ModelAutocorrelationGDPResidual Analysis
- Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (1994) Time Series Analysis: Forecasting and Control. 3rd Edition, Prentice Hall, Englewood Cliffs, NJ.
- Firmino, P.R.A., de Mattos Neto, P.S.G. and Ferreira, T.A.E. (2015) Error Modeling Approach to Improve Time Series Forecasters. Neurocomputing, 153, 242-254. https://doi.org/10.1016/j.neucom.2014.11.030
- Ikughur, A.J., Uba, T. and Ogunmola, A.O. (2015) Application of Residual Analysis in Time Series Model Selection. Journal of Statistical and Econometric Methods, 4, 41-53. http://www.scienpress.com/Upload/JSEM%2fVol%204_4_3.pdf
- McLeod, A.I. and Li, W.K. (1983) Diagnostic Checking ARMA Time Series Models Using Squared-Residual Autocorrelations. Journal of Time Series Analysis, 4, 269-273. https://doi.org/10.1111/j.1467-9892.1983.tb00373.x
- Lu, Y. (2009) Modeling and Forecasting China’s GDP Data with Time Series Models. D-Level Essay in Statistics. Department of Economics and Society, Hogskolan Dalarna, Sweden.
- Andreii, E.A. and Bugudui, E. (2011) Econometric Modeling of GDP Time Series. Theoretical and Applied Economics, 18, 91-98. http://store.ectap.ro/articole/652.pdf
- Okyere, F., Mahama, F., Yemidi, S. and Krampa, E. (2015) An Econometric Model for Inflation Rates in the Volta Region of Ghana. IOSR Journal of Economics and Finance, 6, 48-55.
- Boshnakov, G.N. (2016) Introduction to Time Series Analysis and Forecasting. 2nd Edition, John Wiley and Sons, Hoboken.
- Lavrenz, S.M., Vlahogianni, E.I., Gkritza, K. and Ke, Y. (2018) Time Series Modeling in Traffic Safety Research. Accident Analysis & Prevention, 117, 368-380. https://doi.org/10.1016/j.aap.2017.11.030
- Martin, J., de Adana, D.D.R. and Asuero, A.G. (2017) Fitting Models to Data: Residual Analysis, a Primer, Uncertainty Quantification and Model Calibration. Jan Peter Hessling, IntechOpen. https://www.intechopen.com/books/uncertainty-quantification-and-model-calibration/fitting-models-to-data-residual-analysis-a-primer
- Frost, J. (2012) Why You Need to Check Your Residual Plots for Regression Analysis.
- Brockwell, P.J. and Davis, R.A. (2002) Introduction to Time Series and Forecasting. 2nd Edition, Springer, New York. https://doi.org/10.1007/b97391
- Pasavento, E. (2007) Residuals-Based Tests for the Null of No-Co-Integration: An Analytical Comparison. Journal of Time Series Analysis, 28, 111-137. https://doi.org/10.1111/j.1467-9892.2006.00501.x