Abstract
The analysis of time series considers one of the mathematical and statistical methods in explanation of the nature phenomena and its manner in a specific time period. Because the studying of time series can get by building, analysis the models and then forecasting gives the priority for the practicing in different fields, therefore the identification and selection of the model is of great importance in spite of its difficulties. The selection of a standard methods has the ability for estimation the errors in the estimated the parameters for the model, and there will be a balance between the suitability and the simplicity of the model. In the analysis of data there are many suitable models that can be taken for representation in certain groups of data (autocorrelation function ACF, partial autocorrelation function PACF and inverse autocorrelation function IACF). In this search a comparison was done using generated data (simulation) and practical application, namely, the data of some climate elements in Iraq (sun-shine, temperatures and relative humidity). Where some new results are obtained.
DOI
10.33095/jeas.v13i48.1228
Subject Area
Statistical
First Page
251
Last Page
272
Recommended Citation
Al-Nasser, A. H., & Juma, A. A. (2007). A Comparison of Methods for Order Selection in Normal Autoregressive Models: Using Simulated Data and Selected Climatic Variables in Iraq. Journal of Economics and Administrative Sciences, 13(48), 251-272. https://doi.org/10.33095/jeas.v13i48.1228
