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Re: st: Nonlinear least squares restrictions
From
"G. Anderson" <[email protected]>
To
[email protected]
Subject
Re: st: Nonlinear least squares restrictions
Date
05 Jun 2013 15:34:09 +0100
Hi Maarten,
Thanks a lot for this- it does sound like this could do the trick (the
example about the contributions of father's and mother's education seems
along the lines I am looking for).
Specifically I am looking at how yields changes in response macro data- and
I want the relative contributions of different data releases to be constant
over time, but allow the overall magnitude to vary annually.
So for example if we consider gdp, unemployment and inflation as our macro
data releases- I want their relative contributions to yield changes to be
fixed over time. But the overall magnitude of how they affect yields can
vary annually.
Is the right setup using propcnsreg:
yield dum00 dum01 dum02 dum03 dum04 dum05 ,constrained(gdp unemployment
inflation) lambda(dum00 dum01 dum02 dum03 dum04 dum05)
Where dum are annual dummies.
In particular- do I include the annual dummies as both independent
variables and variables in lambda? Is it also possible to impose a
normalization so that on average the coefficients on the dummies is equal
to 1?
Many thanks for your help with this,
Gareth
On Jun 5 2013, Maarten Buis wrote:
Sounds to me like a proportionality constraint. I have implemented
that in -propcnsreg-, which you can download by typing in Stata -ssc
install propcnsreg-. There is a description of that type of models
here: <http://www.maartenbuis.nl/wp/prop.html>
-- Maarten
On Wed, Jun 5, 2013 at 1:23 PM, G. Anderson <[email protected]> wrote:
Hi,
Using daily data, I am trying to estimate a relationship of the form y=a
+bBX +u
Where a is a scalar which can vary across years, b is a scalar which can
vary across years, X is a vector of data and B is a vector of parameters
which is fixed across years.
Therefore I am trying to estimate a, b and B. I want to normalise b so
that on average it is equal to 1 across years.
I have tried experimenting using the nonlinear regression function but so
far to no avail- is this type of restriction possible in Stata?
Many Thanks,
Gareth
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