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diff --git a/octave_packages/econometrics-1.0.8/mle_results.m b/octave_packages/econometrics-1.0.8/mle_results.m
new file mode 100644
index 0000000..f931c85
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+++ b/octave_packages/econometrics-1.0.8/mle_results.m
@@ -0,0 +1,89 @@
+# 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
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# 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 = char("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 = {-1}, nslaves = 0)
+	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);
+    		endif
+	endif
+
+	[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";
+	endif
+	printf("\n\n******************************************************\n");
+	disp(mletitle);
+	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 =[theta, se, theta./se, 2 - 2*normcdf(abs(theta ./ se))];
+
+	clabels = char("estimate", "st. err", "t-stat", "p-value");
+
+	printf("\n");
+	if names !=0 prettyprint(a, names, clabels);
+	else prettyprint_c(a, clabels);
+	endif
+
+	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);
+	printf("******************************************************\n");
+endfunction