wald test or test of equality of means

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ARS
ARS 2012-10-7
Hi,
I am interested in finding a test of equality of two means (mu1=mu2). Is there any function/command in matlab through which I could run the regression:
*Wage = B1 (USD) + B2 (EURO) + e *
and find t-stats for all coefficients as well as a test of equality of means of B1 and B2 .
Please also tell if there is any GUI through which all this is manageable with a few clicks....as in EVIEWS.
Regards,
AMD.

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回答（3 个）

Daniel Shub
Daniel Shub 2012-10-8
No. While the MATLAB stats toolbox is powerful, it is not point and click powerful like EVIEWS or SPSS.
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ARS
ARS 2012-10-8
Hi Daniel,
So, whats the best way to do the above using matlab syntax?
Cheers AMD

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Tom Lane
Tom Lane 2012-10-8
I assume you mean you want to test the equality of two regression coefficients. This will work in the current release of the Statistics Toolbox:
USD = rand(100,1);
EURO = rand(100,1);
Wage = 10*USD - 7*EURO + randn(100,1);
lm = LinearModel.fit([USD EURO],Wage)
[p,F] = coefTest(lm,[0 1 -1])
It fits a linear regression model and displays a table with t-statistics. The last command tests whether the two slopes are coefficient, returning a p-value and F-statistic for the test. Note this includes an intercept in the model. You can remove it if you want, but that's the default.
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ARS
ARS 2012-10-8
Hi,
I am using Matlab 2011b and it given an error
""Undefined variable "LinearModel" or class "LinearModel.fit"."" What should I do now?
Please also tell that there are a few procedures like
  • nwest by Kevin shepard
  • olsnw
  • regstats2 by Oleg Komarov
  • regstats
and as you mentioned linearmodel.fit.....which one is the most reliable, I mostly have to do time series regressions and I use HAC, white standard errors....so which procedure is more flexible.
The one you mentioned is giving error.
Regards,
AMD.

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Tom Lane
Tom Lane 2012-10-10
In an older release, check out "help linhyptest". It can perform a hypothesis test on coefficients given estimates and a covariance matrix. Using regstats you can get the estimate beta and covariance matrix covb. Perhaps the other procedures will also provide these outputs.

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