Fama-MacBeth regression

[metrix]

I thought that this line

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equation {%subcsavg}.ls(cov=hac) avgrets c betag

I have change it by this code (Ref: EViews 8 command ref pp 382 Panel LS Options)

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equation {%subcsavg}.ls(wgt=cxsur) avgrets c betag

but an error message appear.
I don't know what to say thanks for the help.

[EViews Gareth]

You don't have a panel.

[metrix]

no idea , can you help me.

[EViews Gareth]

Looks like the subroutine FMB is the part that does the calculations. The line:

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rowplace(g, @transpose(@inverse(@transpose(design) * design) * @transpose(design) * retvec), j)

is the one that performs OLS and puts the row of coefficients into the matrix g. Just change that line to do whatever calculation you want, instead of OLS.

[metrix]

thanks for your help Mr Gareth.

[mick1987]

Hi, I would like some tips on how to modify the fama-macbeth code such that I also get the R squared of the regression. Thanks in advance!

[EViews Gareth]

Which regression?

[mick1987]

The second regression where the cross-sectional regression is done.

[EViews Gareth]

It isn't currently covered by the add-in. As mentioned a few posts above, there is a single subroutine that currently performs the least squares regression (and does so using matrix objects rather than the built-in estimation routines). You would have to modify that subroutine to also calculate, and store, the R-squared.

[mick1987]

Thanks. I have tried to understand how the rolling add-in function in eviews works and also tried to implement it. Do you think I can apply the same method as in the roll code for the add-in?

[mick1987]

Sorry for the diffusing question.. what I'm asking about is that the roll add-in function reports the r squares.. can I use their way to get the r square incorporated into the fama-macbeth add-in??

[mick1987]

Also I have a question regarding the second table output from the add-in function, are the betas reported in table 2 average betas from the first regression?

[Maverick]

Hi, I also need to incorporate r square into the code. I understand the first part of the fmb subprogram, but not the following part of the operation in the program:

@transpose(@inverse(@transpose(design) * design) _
* @transpose(design) * retvec)

If someone can help me understand this part it would be really appreciated! Thanks in advance! Also I would like to know if this sign: _ has a meaning in the code?

[Maverick]

Also it would be nice to understand the purpose of design=1 in the below code?

matrix(n_p, n_f + 1) design = 1

[EViews Rebecca]

Also I have a question regarding the second table output from the add-in function, are the betas reported in table 2 average betas from the first regression?

They are betas from a second regression that use the betas from the first timeseries regression as regressors. See equation 4 in the documentation for more details.