@misc{eprints69534,
           month = {Januari},
           title = {ANALISIS REGRESI RIDGE GENERALIZED LEAST SQUARE (RGLS)
UNTUK MENGATASI MULTIKOLINEARITAS DAN AUTOKORELASI},
          author = {sherlinay65@gmail.com SHERLINA YULIANTI },
         address = {UNIVERSITAS LAMPUNG},
       publisher = {FAKULTAS PERTANIAN  },
            year = {2023},
             url = {http://digilib.unila.ac.id/69534/},
        abstract = {ABSTRACT
RIDGE GENERALIZED LEAST SQUARE (RGLS) REGRESSION
ANALYSIS TO OVERCOME MULTICOLLINEARITY AND
AUTOCORRELATION
By
Sherlina Yulianti
Multiple linear regression analysis is a method for analyzing the relationship
between the dependent variable and several independent variables. The
independent variables in multiple linear regression analysis have a possibility to
correlate with each other or called multicollinearity problems and linear
regression using time series data has a possibility to have error autocorrelation
problems because the data at this time has a relationship with data at the previous
time. Both of these conditions have an adverse effect on estimates and
predictions. The Ridge Generalized Least Square (RGLS) method is able to
overcome multicollinearity and autocorrelation problems simultaneously.
Therefore this study aims to study the performance of the RGLS method in
overcoming multicollinearity and autocorrelation problems through Monte Carlo
simulations with n = 50, 75, and 100 which have 6 independent variables with a
multicollinearity level of 0.99 and an autocorrelation level of 0.15. This study
gives the result that the RGLS method is able to overcome multicollinearity and
autocorrelation problems in the data and it can be concluded that the more the
number of samples used, the smaller the resulting MSE value and the greater the
resulting }
}