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oprobit1

multivariate ordered probit regression

CALLING SEQUENCE

res=oprobit1(y,x,param)

PARAMETERS

Input

* y = (n x 1) dependent variable vector taking ncat integer values

* x = (n x m) explanatory variables matrix

* param = (k x 1) parameter vector

* optfunc = a string, the name of the optimisation function ('optim' or 'optimg')

* opt_optim = a tlist, collecting the options to the optimisation function

 

Output

* res = a results tlist with

  - res('meth') = 'ordered probit'

  - res('y') = (nobs x ncat) matrix of data

  - res('x') = (nobs x nx) matrix of data

  - res('nobs') = number of observations

  - res('nvar') = number of variables

  - res('ncat') = number of categories of dependent variable (including the reference category j = 0)

  - res('beta') = (nvar*ncat x 1) vector of beta coefficients: [beta_1 ; beta_2 ; ... ; beta_ncat] under normalization beta_0 = 0

  - res('yhat') = (nobs x ncat) matrix of fitted values probabilities: [P_0 P_1 ... P_(ncat-1)] where P_j = [P_1j ; P_2j ; ... ; P_nobsj]

  - res('r2mf') = McFadden pseudo-R^2

  - res('rsqr') = Estrella pseudo-R^2

  - res('llike') = unrestricted log likelihood

  - res('lratio') = LR test statistic against intercept-only model (all bets=0), distributed chi-squared with (nvar -1)*ncat degrees of freedom

  - res('covb') = (nvar*ncat x nvar*ncat) covariance matrix of coefficients

  - res('tstat') = (nvar*ncat x 1) vector of t-statistics

  - res('pvalue') = (nvar*ncat x 1) vector of corresponding p-values

DESCRIPTION

Computes multivariate ordered probit regression.

EXAMPLE

load(GROCERDIR+'\data\gunther.dat')
[y,namey,x]=explouniv('rrating',['asset' 'equ' 'growth' 'loa' 'metro' 'prl'])
res=oprobit1(y,x,[zeros(6,1);-1;0;1])

AUTHOR

Eric Dubois 2007

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