Dear all,

I want to estimate betas of the Fama French Model for every year. I have got weekly returns and I am using the following loop:

foreach variable of varlist r_share* {

sum `variable' if year == 1991

if r(N) >= 52 {

scalar lagged = floor(4*(r(N)/100)^(2/9))

newey `variable' r_sp500 smb hml if year == 1991, lag(`= lagged')

}

else {

display "Not enough observations"

}

matrix matrix_1991 = nullmat(matrix_1991) \ e(b)

}

Variables:

r_share = return share

smb = Fama French factor

hml = Fama French factor

r_sp500 = return s&p500

The loop is working fine there is just one problem and I am not able to solve this. As I need all the estimated betas in a matrix afterwards (matrix_1991) and I have got missing observations (for the Y-values) how can I tell stata to save "." in the matrix when there are "Not enough observations"?

So if I have got enought observations Stata should do the newey west regression and if not it should just store . for all coefficients.

Hopefully somebody can help me out!

BR

Ikn

I want to estimate betas of the Fama French Model for every year. I have got weekly returns and I am using the following loop:

foreach variable of varlist r_share* {

sum `variable' if year == 1991

if r(N) >= 52 {

scalar lagged = floor(4*(r(N)/100)^(2/9))

newey `variable' r_sp500 smb hml if year == 1991, lag(`= lagged')

}

else {

display "Not enough observations"

}

matrix matrix_1991 = nullmat(matrix_1991) \ e(b)

}

Variables:

r_share = return share

smb = Fama French factor

hml = Fama French factor

r_sp500 = return s&p500

The loop is working fine there is just one problem and I am not able to solve this. As I need all the estimated betas in a matrix afterwards (matrix_1991) and I have got missing observations (for the Y-values) how can I tell stata to save "." in the matrix when there are "Not enough observations"?

So if I have got enought observations Stata should do the newey west regression and if not it should just store . for all coefficients.

Hopefully somebody can help me out!

BR

Ikn

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