Dear all, First of all I am not proffessional in R and sorry if my question is not very good formulated. So I have a problem I want to estimate a constrained regression with a weighted least square estimator (Rouwenhorst modell 1994). In fact it is a regression with dependent variable as index return and the independent variables are Dummyvariables. index_returns(ij)=a + sum(beta(i)*I(i))*w(i))+sum(gama(j)*C(j)*v(j)) + e(ij) with index_returns in country j and Industry i (I have Industry indexes country-specific, that means I have more observations than parameters to estimate) and a=global return same for all industries and countries, beta(i)=Coefficient for Industry I(i). I(i) is one if index return is from industry i. C(j) is one if index return is from country j. Gamma(j) the Coefficient for Country j. w(i) is the market value of industry i divided by the total market capitalization in time t. v(i) the market value of country j divided by the total market capitalization in time t. In order to avoid multicolinearity two linear constraints are integrated: sum(w(i)*beta(i)=0 sum(v(j)*gamma(j)=0 So this is my problem. A Cross sectional constrained weighted least square regression. I tried it for example with Quadratic Programming. But I am not sure how to integrate constraints and by the way I need standard errors. This for my master thesis and since everything i wanted to analyse is build up on this Regression, i am quite stucked, since i tried everything. Could Somebody help me Kind regards Phillip -- View this message in context: http://r.789695.n4.nabble.com/constrained-weighted-least-square-estimation-tp4205373p4205373.html Sent from the Rmetrics mailing list archive at Nabble.com.
constrained weighted least square estimation
1 message · Philipp