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The research presented a comparative study in time series analysis and forecasting using VAR models, which depend on the existence of a significant relationship between the studied variables, and ARIMAX models, which depend on the linear effect of the independent variables (model input) on the dependent variable (model output). The models were anal...
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This study involves a comparison between the application of the univariate SARIMA model and the utilization of VAR methods (vector autoregressive models) for multivariate time series analysis. The analysis is conducted using three-time series variables derived from data representing the monthly average of Humidity (H), Rainfall (R), and Temperature...
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... Furthermore, some researchers compared VAR and ARIMAX models for time series analysis and forecasting using data from the Iraqi general budget (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020), concluding that the VAR model was more efficient. Forecasts for 2021-2024 indicated a continued increase in foreign reserves and government spending (E.A. Haydier et al., 2023). Because forecasts from VAR models may be formed conditional on the likely future courses of certain model variables, they are extremely flexible. ...
This study investigates the VAR time series data of the overall expenditures and income in the Kurdistan Region of Iraq. It applies multivariate wavelet shrinkage within the VAR model, comparing it to traditional methods to identify the most appropriate model. The chosen model will then be used to predict general expenditures and revenues for the years 2022-2026. The analysis involved assessing the stationarity of the expenditure and revenue time series, which are interrelated variables during the interval 1997-2021, and identifying the overall trend through differencing to achieve stationarity. The proposed method incorporated multivariate wavelet shrinkage in the VAR model to address data contamination in expenditures and revenue using various wavelets like Coiflets, Daubechies, Symlets, and Fejér-Korovkin at different orders. Threshold levels were estimated using the SURE method and soft thresholding rules to denoise the data for the following analysis within the VAR model. Model selection was based on Akaike and Bayes information criteria. The analysis, conducted using MATLAB, indicated the superiority of the proposed method over traditional methods, forecasting a continued rise in expenditures and revenues for the Iraqi Kurdistan region from 2022 to 2026. The findings suggest that advanced techniques can offer more accurate economic forecasts, benefiting regional planning and policy-making.
The statistician Fisher developed the statistical method of analysis of variance
symbolised by (ANOVA) through the analysis of experimental data, and his first use
was in agricultural experiments based on data representing different agricultural
fertilisers as well as different seeds, while now the use of variance analysis has extended
to include all different scientific disciplines.
The method of analysis of variance is a statistical test based on the F-distribution by
obtaining differences or total variance that consist of several separate components,
which are the causes or sources (Sources) of variance to be analyzed, and this method
is not much different from regression analysis in terms of objective, however, the main
difference is that regression analysis gives numerical values for the effect of
independent variables on the follower, while the analysis of variance is sufficient to
indicate that these variables affect the dependent variable or not.
The Statistical Package for Social Science, known as SPSS, is one of the most common and used ready-made statistical programs by researchers and students in many different fields, including administrative, economic, accounting, social, educational, medical, engineering, agricultural, chemical, etc.
The statistician Fisher developed the statistical method of analysis of variance
symbolised by (ANOVA) through the analysis of experimental data, and his first use
was in agricultural experiments based on data representing different agricultural
fertilisers as well as different seeds, while now the use of variance analysis has extended
to include all different scientific disciplines.
The method of analysis of variance is a statistical test based on the F-distribution by
obtaining differences or total variance that consist of several separate components,
which are the causes or sources (Sources) of variance to be analyzed, and this method
is not much different from regression analysis in terms of objective, however, the main
difference is that regression analysis gives numerical values for the effect of
independent variables on the follower, while the analysis of variance is sufficient to
indicate that these variables affect the dependent variable or not.
2025 2 Introduction In practical applications, especially economics, we find that simple linear regression is very rarely used and that most applications include estimating the relationship between more than two variables, that is, the presence of more than one independent variable with one dependent variable, which is included in the multiple linear regression model, so this model is considered a generalization of the simple linear regression model. And success comes from God
2025 2 Introduction Usually, the researcher needs to ensure the efficiency of the standard model that has been estimated and the extent of its conformity with the economic theory and test the significance of the estimated parameters using the method of ordinary least squares (OLS) and on this basis, there are three types of criteria, namely : 1-Theoretical economic criterion : The initial criterion used is the extent to which the size and indicators of the parameters estimated in the standard model conform to economic theory and its relationships. Which is determined and defined by economic theory at the stage of characterization of the standard model (which is the first stage of economic measurement research). 2-Standard Criteria : The theory of economic measurement determines this criterion with the goal defined for all hypotheses used and on the extent to which statistical criteria can be used, such as the hypothesis of one of the values of the random error variable is not related to the other values (the problem of autocorrelation of random error). 3-Statistical Criterion: This criterion is determined by statistical theory and its goal is to assess the degree of dependence on the estimated parameters of the standard model, i.e. the use of statistical tests such as the T-test and the F-test to find out the significance of the estimated parameters or statistical model. And success comes from God
It is sure that your paper will be included in the coming issue of PJS i.e. Volume 41 No.2 (April 2025).
However, we will be waiting your next response about the APC amount, please.
2025 2 Introduction This chapter dealt with one of the conditions that must be met in estimating the parameters of the multiple models, which is the absence of the phenomenon of numerous linear relationships (multi-collinearity) between independent variables and this compound reformer consists of numerous (Multi) and common or interconnected (Co) and linear (Linearity). Statistician Frisch is the first to discover this phenomenon in economic studies, which are usually time series, where cases of multiple linear interference with economic variables appear since some independent variables may develop during a certain time To be affected by multiple economic factors.
In general, the economic model is a collection of formulas that translate economic
theory based on statistical and mathematical methods to explain the structural makeup
of a particular industry or the national economy. Alternatively, the standard model
provides a numerical representation of the country's or businesses' economic activity
during a given time. Consequently, economic theory determines the model's equation
or set of equations and its mathematical form, whereas economic measurement
establishes the best format for the data of economic variables based on their linearity
or non-linearity, thereby estimating the model's parameters. This chapter will discuss
the simple linear model, i.e. the model that includes a dependent variable and an
independent variable, or what is called the explanatory variable, in addition to the
presence of a random error variable or what is called the unexplained variable.
In general, the economic model is a collection of formulas that translate economic
theory based on statistical and mathematical methods to explain the structural makeup
of a particular industry or the national economy. Alternatively, the standard model
provides a numerical representation of the country's or businesses' economic activity
during a given time. Consequently, economic theory determines the model's equation
or set of equations and its mathematical form, whereas economic measurement
establishes the best format for the data of economic variables based on their linearity
or non-linearity, thereby estimating the model's parameters. This chapter will discuss
the simple linear model, i.e. the model that includes a dependent variable and an
independent variable, or what is called the explanatory variable, in addition to the
presence of a random error variable or what is called the unexplained variable.
