ESTIMATING CO-INTEGRATING REGRESSION IN TIME SERIES | ||||
| Fayoum Journal of Agricultural Research and Development | ||||
| Article 21, Volume 33, Issue 1, January 2019, Page 92-102 PDF (2.46 MB) | ||||
| Document Type: Research articles. | ||||
| DOI: 10.21608/fjard.2019.190383 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Rania Fikry Mahmoud | ||||
| Cent. Lab. for Design and Stat. Analysis Res., ARC. | ||||
| Abstract | ||||
| The study investigated the equilibrium relation, on the long run, between the local production (GDP) and the index number for the wholesale prices during the period 1986-2016 and identifying their integration degree so that some methods could be used to estimate and compare the regression of cointegration to create accurate estimations with a high degree of confidence. Prediction of greater precision and the study of the unit root test for the stability of time series show that the time series for both the index number of wholesale prices and the local production (GDP) are unstable at its level at the first difference while it was stable at the second difference. The latent root maximization test showed that there was at least one vector of co-integration. Three methods of joint integration were used in comparison with the ordinary least squares method namely: the modified least squares method, the method of regression of the common integers and the lower dynamic squares method. The method of dynamic least squares was the best way to estimate the regression of co-integration since it gave the best results in interpreting the relationship between the two variables. The study recommends further studies in which this finding could be confirmed when using a larger number of independent variables as well as directing research to apply these methods when studying and analyzing the behavior of time series. Also, conducting further research in the field of time series stability, causal relationships and methods of co-integration regression on the short and long term. | ||||
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