Dear R Helpers,


I would like to run a constrained weighted OLS regression in R ( Heston and
Rouwenhorst Model 1995).
With weighted I mean that the estimator allows for different variances of
the disturbance term (Weighted Least squares).  But this is possible to
specify in lm. The problem is that I want to add two constrains to the the
coefficients of the model.

I have the following model: Return ~  EU + AUS + ASIA + USA + office +
retail + industrial + residential.

The model consist of country and sector dummies with two constrains:

-The sum of the coefficients of the property types dummies should equal
zero.=> office + retail + industrial + residential = 0
-The sum of the  coefficients of the country dummies should equal zero.=> EU
+ AUS + ASIA + USA = 0

Is this possible to do this in the lm framework?

I already searched the archives and tryed some packages but it's stil not
clear how I can do this regression easily and fast.

Thanks in advance,

Pieter Plaizier
0031646160041






Hi Grant,

sorry I mean with weighted that the estimator allows for different variances
of the . The problem is that I want to add tdisturbance term (Weighted Least
squares). But that's possible to specify in lmwo constrains to the the
coefficients of the model.

I have the following model: Return ~ EU + AUS + ASIA + USA + office + retail
+ industrial + residential.
- The sum of the coefficients of the property types dummies should equal
zero. EU + AUS + ASIA + USA = 0  .
- The sum of the coefficients of the region dummies should equal zero.
office + retail + industrial + residential =0

Thanks in advance( I will also email the R-sig-finance mailing list).

Kind regards,

Pieter Plaizier

Hi Peter,
I haven't used this method.  You say the sum of the "weighted
coefficients"?  I'm not sure exactly what you mean by that and I don't have
the paper at the moment.
I will try and look into this after work, though you may get a better and
faster answer my mailing the R-sig-finance mailing list or one of the other
R mailing lists.
Good luck,

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