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Asked by Marcin Kuc on 1 Aug 2011

Hi,

I would like to perform a simple econometric regression using Matlab, but I have been struggling to do it for a few days now. Quite simply, I have a set of data for both X and Y. I would like to perform a regression of the following form: Y= BetaZero + Beta*X + ErrorTerm. Could anyone please tell me what is the right function to do so? I've been trying to accomplish that by using regress(), but I realised that it does not return the value of the constant term (aka Beta zero or alpha). Thank you very much for your help.

Answer by Oleg Komarov on 1 Aug 2011

Accepted answer

Use regstats then with the same inputs:

regstats(y,x,'linear')

Marcin Kuc on 1 Aug 2011

Perfect. Thanks a lot. It gives so much information and I was wondering how is it different to:

Betas = [ones(length(X),1) X] \ y

Shashank on 12 Jan 2013

If you open up regstats or regress in MATLAB using the edit command you will see that it does the same as what you describe with \ operator. An alternative and recommended approach is the new LinearModel.fit(X,y) which will give you the result and the fit statistics. Make sure you are using MATLAB R2012a and above

Answer by Fangjun Jiang on 1 Aug 2011

You have the right function. Just try it with three return variables.

[B,BINT,R] = REGRESS(Y,X) returns a vector R of residuals.

Read the full text of help regress

Marcin Kuc on 1 Aug 2011

Ok, as for the results that it returns, which ones will be beta0 and beta1? I believe B should be Beta1 and R is an array with residuals. Where's the constant term?

Oleg Komarov on 1 Aug 2011

You have to add a column of ones for the constant. As already suggested read the documentation.

Marcin Kuc on 1 Aug 2011

I tried to understand it, but it appears to be far too complicated for typical Eviews users. I will definitely dive into it after I finish my current project.

However, I would be very grateful if you could tell me exact way of getting the constant. Thank you in advance.

Answer by Fangjun Jiang on 2 Aug 2011

To use regress();

%% x=(1:100)'; y=10+2*x+rand(100,1);

[B,BINT,ErrorTerm]=regress(y,[ones(size(x)),x]); BetaZero=B(1) Beta=B(2)

BetaZero =

10.6381

Beta =

1.9976

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