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Sourcecode: octave-econometrics version File versions


# Copyright (C) 2003,2004,2005  Michael Creel <michael.creel@uab.es>
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program; If not, see <http://www.gnu.org/licenses/>.

# usage: [theta, V, obj_value, infocrit] =
#    mle_results(theta, data, model, modelargs, names, title, unscale, control)
# inputs:
# theta: column vector of model parameters
# data: data matrix
# model: name of function that computes log-likelihood
# modelargs: (cell) additional inputs needed by model. May be empty ("")
# names: vector of parameter names, e.g., use names = str2mat("param1", "param2");
# title: string, describes model estimated
# unscale: (optional) cell that holds means and std. dev. of data (see scale_data)
# control: (optional) BFGS or SA controls (see bfgsmin and samin). May be empty ("").
# nslaves: (optional) number of slaves if executed in parallel (requires MPITB)
# outputs:
# theta: ML estimated value of parameters
# obj_value: the value of the log likelihood function at ML estimate
# conv: return code from bfgsmin (1 means success, see bfgsmin for details)
# iters: number of BFGS iteration used

## Please see mle_example for information on how to use this

# report results
function [theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, mletitle, unscale, control, nslaves)
      if nargin < 9 nslaves = 0; endif # serial by default
      if nargin < 8 control = {-1}; endif
      if nargin < 6 mletitle = "Generic MLE title"; endif

      [theta, obj_value, convergence] = mle_estimate(theta, data, model, modelargs, control, nslaves);
      V = mle_variance(theta, data, model, modelargs);

      # unscale results if argument has been passed
      # this puts coefficients into scale corresponding to the original modelargs
      if (nargin > 6)
            if iscell(unscale) # don't try it if unscale is simply a placeholder
                  [theta, V] = unscale_parameters(theta, V, unscale);

      [theta, V] = delta_method("parameterize", theta, {data, model, modelargs}, V);

      n = rows(data);
      k = rows(V);
      se = sqrt(diag(V));
      if convergence == 1 convergence="Normal convergence";
      elseif convergence == 2 convergence="No convergence";
      elseif convergence == -1 convergence = "Max. iters. exceeded";
      printf("\nMLE Estimation Results\n");
      printf("BFGS convergence: %s\n\n", convergence);

      printf("Average Log-L: %f\n", obj_value);
      printf("Observations: %d\n", n);
      a =;

      clabels = str2mat("estimate", "st. err", "t-stat", "p-value");

      if names !=0 prettyprint(a, names, clabels);
      else prettyprint_c(a, clabels);

      printf("\nInformation Criteria \n");
      caic = -2*n*obj_value + rows(theta)*(log(n)+1);
      bic = -2*n*obj_value + rows(theta)*log(n);
      aic = -2*n*obj_value + 2*rows(theta);
      infocrit = [caic, bic, aic];
      printf("CAIC : %8.4f      Avg. CAIC: %8.4f\n", caic, caic/n);
      printf(" BIC : %8.4f       Avg. BIC: %8.4f\n", bic, bic/n);
      printf(" AIC : %8.4f       Avg. AIC: %8.4f\n", aic, aic/n);

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