I was using a fixed-effects panel model with interaction effects when I realized that the VIF values are too high for some variables. I was advised to standardize the predictor variables to mitigate multi-collinearity. My question is that can I standardize just 1 predictor variable or must I...
Hello Talk Stats Forum!
I am working on building a Predictive Model using the binary logistic regression and LASSO logistic regression. In order to apply these models on some real data set I am using R software.
Before performing such a model I am testing for multicollinearity and will remove...
I am checking for multicollinearity. And I cant find on the web when having the outpur from R commander, on which I have to look to identify collinearity?
-GVIF- or -df- or -GVIF^2(1/(2*Df))-
Thanks for the help.
I'm currently writing my thesis about the performance of Ethical Companies.
I'm runnning a Fama Macbeth Cross Sectional Regression as in the picture attached.
The problem is that ESG score is correlated with Firm Size, and when I include firm size the alpha(1) changes a lot (turns from...
I came up with this code to calculate VIF for variables for each model I am evaluating
for (i in 1:length(x))
where x is
and where d1...
I am trying to find out whether multicollinearity involving the intercept is taken into account in SPSS' calculation of the VI in the REGRESSION procedure. I was unable to find an explanation in the official SPSS resources as to how SPSS calculates the VIF.
The Command Syntax reference only...
I'm really confused about why I have such high VIFs in a multiple regression I've run. My predictors are country (2 different countries, so -1 and 1), religiosity, and political engagement, and I'm looking at how my three predictors and their various interactions predict an outcome variable...
I have a model with a dichotomous DV and IVs both continuous/numerical and dichotomous.
I checked for collinearity with Pearson.
How do I check for multicollinearity in logistic regression?
Could I follow the procedure of rotating my IVs as DVs one at a time against the remaining IVs, and...
I am having trouble interpreting some of my results.
I am using logistic regression to infer a model based on measured data. Some of my explanatory variables are continuous (e.g. temperature [°C]) and some are categorical (e.g. time of day [night, morning, day, afternoon...
I understand that VIF = 1 / (1-R^2)
If I only have the parameter estimates table with the intercept, X1, and X2 and also the correlation coefficient between X1 and X2, how can I calculate VIF for X1 and X2?