I am using GMM technique for panel data. I have eight variables in my model. For some varaibles, I am coming across different signs of correlation and regression coefficients like, one of my variable has a positive coefficient in the regression model but when I am plotting the correlation matrix, it is showing a negative coefficient of correlation with the dependent variable. I need to know if this is a point of concern for the model, if it is, then what can be the solution to this problem?
Not a problem. They just measured different things. r tels you how the 2 variables vary together while regression coefficient says rate of change of y per 1 unit of x. In a multiple regression the signs can be different as you have observed because of the effects of the the other IVs. Best wishes, David Booth
Hello Zaira,
David Eugene Booth 's answer is absolutely correct. If it turned out to be the case that all of your independent variables were uncorrelated with one another, then you'd never see a result in which regression coefficients took on a different sign than the zero-order (ordinary Pearson) correlations with the DV.
However, when there is redundancy among the IVs, it is quite possible that both the magnitude and sign of regression coefficients change relative to what you might have anticipated based on the zero-order correlations.
Good luck with your work.
Check the multicollinearity between the independent variables and also try to apply simple linear regression between the dependent and each independents variable.
Best regards,
