Regression with dummy variable
Posted: Mon Jul 15, 2013 11:01 am
Hello everyone,
I'm trying to run my regression model which is as follows:
Y = a + β1 X1 + β2 X2 + β3 X3 + β4 X4 + β5 X5 + e
Where;
· Y is Internet Financial Reporting index;
· a is constant
· X1 is company size (Log of the book value of total assets)
· X2 is leverage / Debt ratio (total liabilities divided by total assets)
· X3 is profitability/ return on equity (net profit divided by equity)
· X4 is liquidity (Cash divided by total assets)
· X5 is a dummy variable for audit firm size (International audit firm = 1 and small audit firm
· e is error term.
Question:
1) Is my ratios/data set to be used on Eviews appropriate? (kindly find attached data set)
2) Please confirm when i click on - quick - estimate equation , my equation should be as follows:
ifr c cosize lever profit liqui audit
OR for dummy i should write it in another way?
3) I run my data set with the above equation and window appeared indication ''Near singular matrix error. Regressors may be perfectly collinear). Please help.
Many thanks in advance.
I'm trying to run my regression model which is as follows:
Y = a + β1 X1 + β2 X2 + β3 X3 + β4 X4 + β5 X5 + e
Where;
· Y is Internet Financial Reporting index;
· a is constant
· X1 is company size (Log of the book value of total assets)
· X2 is leverage / Debt ratio (total liabilities divided by total assets)
· X3 is profitability/ return on equity (net profit divided by equity)
· X4 is liquidity (Cash divided by total assets)
· X5 is a dummy variable for audit firm size (International audit firm = 1 and small audit firm
· e is error term.
Question:
1) Is my ratios/data set to be used on Eviews appropriate? (kindly find attached data set)
2) Please confirm when i click on - quick - estimate equation , my equation should be as follows:
ifr c cosize lever profit liqui audit
OR for dummy i should write it in another way?
3) I run my data set with the above equation and window appeared indication ''Near singular matrix error. Regressors may be perfectly collinear). Please help.
Many thanks in advance.