Normality tests: a study of residuals obtained on time series tendency modeling
DOI:
https://doi.org/10.5585/2023.22928Keywords:
Normality Tests, Residual analysis, Time Series modelingAbstract
The normality analysis in the distribution of residuals is a determining criterion to verify and validate a model. For example, in modeling financial time series by linear regression, the residuals should be independent of each other, identically distributed, have a normal distribution, and be homoscedastic. Thus, it was aimed to study the performance of some normality tests applied on the residuals obtained from linear regression modeling of time series tendency using polynomials of different degrees. The Jarque-Bera, Anderson-Darling, Kolmogorov-Smirnov-Lilliefors, Doornik-Hansen, and Shapiro-Wilk tests were used, there was agreement in almost all test results, with the exception of the Doornik-Hansen test.
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