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theil1

Theil-Goldberger mixed estimator

CALLING SEQUENCE

[results]=theil1(y,x,rvec,rmat,v)

PARAMETERS

Input

* y = a (N X 1) vector

* x = a (N X k) vector

* rvec = a vector of prior mean values, (c above)

* rmat = a matrix of rank(r) (R above)

* v = prior variance-covariance (var-cov(U) above)

Output

* results = a results tlist with

   - results('meth') = 'Theil-Goldberger'

   - results('beta') = bhat

   - results('y') = y data vector

   - results('x') = x data matrix

   - results('nobs') = # observations

   - results('nvar') = # variables

   - results('yhat') = yhat

   - results('resid') = residuals

   - results('vcovar') = estimated variance-covariance matrix of beta

   - results('sige') = estimated variance of the residuals

   - results('sigu') = sum of squared residuals

   - results('ser') = standard error of the regression

   - results('tstat') = t-stats

   - results('pvalue') = pvalue of the betas

   - results('dw') = Durbin-Watson Statistic

   - results('condindex') = multicolinearity cond index

   - results('prescte') = boolean indicating the presence or absence of a constant in the regression

DESCRIPTION

Computes Theil-Goldberger mixed estimator y = X B + E, E = N(0,sige*IN) c = R B + U, U = N(0,v).

EXAMPLE

xmat = rand(100,4,'n');
exo = [ones(100,1),xmat];
evec = rand(100,1,'n');
 
endo = exo*ones(5,1)+evec;
r = theil1(endo,exo,ones(5,1),eye(5,5),0.1*eye(5,5))
// display the estimated parameters
r('beta')

AUTHOR

Éric Dubois 2019

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