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Hello

I am trying to fit a regression model for attached data.The problem I'm having is that I get back coefficient value of 1 for all independent variables. Maybe my process is wrong. Here's what I'm doing and I wondered if anyone can help me please.

online_visits and offline_visits are my independent variables.
total_visits is my dependent variable.

When I run the regression
ls total_visits c online_visits offline_visits

I get a coefficient value of 1 for both online_visits and offline_visits. Both are significant. I'm expecting offline to be lower than online as the values are much lower.

After reading various things online, I looked at Unit Root Test. I found dependent variables are not stationary without the intercept and trend. So I differenced the series to make it stationary.
ls d(total_visits) c d(online_visits) d(offline_visits)

And getting 1 for coefficients.

Correlogram shows significant autocorrelation at lag 1 and partial correlation at lags 1, 2 and 7. So I tried
ls d(total_visits) c d(online_visits) d(offline_visits) ma(1) ar(1) ar(2) ar(7)

but that gives my an error:
Log of non positive number

Can anyone help me on how do I fit the model properly using EViews or why I get 1s for coefficients please?

Many thanks
Emily

You're estimating an identity.

A regression model would be:

Total_Visits = Constant + Beta0*Online_Visits + Beta1*Ofline_visits + error_term

Where the error term has random noise in it.

Your data actually has this:

Total_Visits = Online_Visits + Ofline_visits


Which implies a few things:
1) Constant = 0
2) Beta0 = 1
3) Beta1 = 1
4) error_term = 0

Since there is no error_term, this isn't something you should be estimating. Least squares is not appropriate.

Ahaaa.. thank you soo much Gareth :D. That makes sense.