CONDUCTING MODEL DIAGNOSTICS: CHECKING THE STATISTICAL CONSISTENCY OF VARIABLES
Keywords:
Model diagnostics, statistical fit, econometrics, regression model, t-statistics, p-value, F-test, coefficient of determination (R²), multicollinearity, heteroskedasticity, autocorrelation, Durbin–Watson test, VIF, statistical significanceAbstract
This article extensively discusses the issues of conducting model diagnostics and checking the statistical consistency of variables in econometric models . The main focus is on diagnostic methods used to assess the reliability and quality of the model, in particular, determining the statistical significance of variables.
In the research process, the influence of variables on the outcome indicator was analyzed based on the regression model, and their significance was assessed using t-statistics and p-values. Also, the overall fit of the model was checked using the F-test, and the explanatory power of the model was determined using the coefficient of determination.
As a result of practical analyses, it was found that some variables were statistically significant, while others were insignificant, justifying the need to improve the model. In addition, methods for identifying and eliminating problems such as multicollinearity, heteroscedasticity, and autocorrelation were considered.
The results of the study show that model diagnostics is an important stage in econometric analysis, which can increase the accuracy and reliability of the model. This approach is of great importance in conducting scientific research and making informed economic decisions
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