Thanks!

I have another problem now, though. All the regressions I try to run like this give me either a standard deviation of 0 for all the estimated parameters, or I get an error message that I have insufficient degrees of freedom. However, I have 8 orthogonality conditions in the example I am trying this out on, and only two parameters. The example is as follows:

# initializations go here
series e1 = 0
series e2 = 0

matrix W = I(8)

scalar alpha = 0
scalar beta = 0

list IVs = const CONSUMPTION_DET(-1) GDP_DETREND(-1) R_DETREND(-1)

gmm
e1 = (alpha / beta) * CONSUMPTION_DET
e2 = (1 / alpha) * GDP_DETREND
orthog e1 ; IVs
orthog e2 ; IVs
weights W
params alpha beta
end gmm

Where CONSUMPTION_DET, GDP_DETREND and R_DETREND are time series with 170 observations.

Why should there be a problem with the degrees of freedom?

Thanks again,

Gal

On Mon, Oct 26, 2009 at 9:09 PM, Allin Cottrell <cottrell@wfu.edu> wrote:

On Mon, 26 Oct 2009, Gal Wettstein wrote:

> I have been trying to run a GMM regression which has two first
> order condition equations. Is this possible with Gretl? If it
> is, how can I do so?

Yes you can do it; you can specify as many orthogonality
conditions as you like. Here's a trivial example in which gmm
wraps OLS done both forwards and backwards:

<script>
open data4-1

matrix b = {50, 0.1, 30, 6}
matrix W = I(4) ./ 5
list X1 = const sqft
list X2 = const price
series e1 = 0
series e2 = 0

gmm
e1 = price - b[1] - b[2]*sqft
e2 = sqft - b[3] - b[4]*price
orthog e1 ; X1
orthog e2 ; X2
weights W
params b
end gmm
</script>

Allin Cottrell
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