econometrics/0000755000175000017500000000000012107462002011704 5ustar nirnireconometrics/INDEX0000644000175000017500000000071512107461241012505 0ustar nirnireconometrics >> Econometrics Econometrics delta_method gmm_estimate gmm_example gmm_obj gmm_results gmm_variance gmm_variance_inefficient mle_estimate mle_example mle_obj mle_results parameterize prettyprint scale_data unscale_parameters nls_estimate nls_obj mle_obj_nodes nls_example poisson poisson_moments prettyprint_c kernel_density kernel_density_cvscore kernel_example kernel_optimal_bandwidth kernel_regression kernel_regression_cvscore econometrics/COPYING0000644000175000017500000010451311753175674012771 0ustar nirnir GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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But first, please read . econometrics/src/0000755000175000017500000000000012107462002012473 5ustar nirnireconometrics/src/__kernel_weights.cc0000644000175000017500000000362012107460775016332 0ustar nirnir// Copyright (C) 2007 Michael Creel // // 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 3 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 . // __kernel_weights: for internal use by kernel density and regression functions // This function was originally written as .m file and eventually rewritten in // C++ for performance. the .m file was removed from the package on revision // 10407 by carandraug #include #include DEFUN_DLD(__kernel_weights, args, ,"__kernel_weights: for internal use by kernel_regression and kernel_density functions") { Matrix data (args(0).matrix_value()); Matrix evalpoints (args(1).matrix_value()); std::string kernel (args(2).string_value()); int n, nn, i, j, k, dim; n = data.rows(); dim = data.columns(); nn = evalpoints.rows(); Matrix W(nn, n); Matrix zz(n,dim); Matrix zzz; octave_value kernelargs; octave_value_list f_return; for (i = 0; i < nn; i++) { for (j = 0; j < n; j++) { // note to self: zz.insert(data.row(j) - evalpoints.row(i), j, 0) is slower for (k = 0; k < dim; k++) { zz(j,k) = data(j,k) - evalpoints(i,k); } } kernelargs = zz; f_return = feval(kernel, kernelargs); zzz = f_return(0).matrix_value(); // note to self: W.insert(zzz.transpose(),i,0) is slower for (j = 0; j < n; j++) { W(i,j) = zzz(j,0); } } f_return(0) = W; return f_return; } econometrics/src/Makefile0000644000175000017500000000024711753201334014143 0ustar nirnirMKOCTFILE = mkoctfile -Wall PROGS = $(patsubst %.cc,%.oct,$(wildcard *.cc)) all: $(PROGS) %.oct: %.cc $(MKOCTFILE) $< clean: rm -f *.o octave-core core *.oct *~ econometrics/NEWS0000644000175000017500000000131112107461241012403 0ustar nirnirSummary of important user-visible changes for econometrics 1.1.1: ------------------------------------------------------------------- ** Fixed a bug that affected gmm_example by making gmm_obj no longer private Summary of important user-visible changes for econometrics 1.1.0: ------------------------------------------------------------------- ** The following functions have been made private: average_moments gmm_obj kernel_density_nodes __kernel_epanechnikov __kernel_normal kernel_regression_nodes mle_variance nls_obj_nodes sum_moments_nodes ** Calls to deprecated functions have been replaced for compatibility with newer octave versions. econometrics/doc/0000755000175000017500000000000012107462002012451 5ustar nirnireconometrics/doc/README0000644000175000017500000000057410472051413013342 0ustar nirnirThe main functions for end users are: mle_estimate.m Performs estimation by MLE mle_results.m Performs estimatiion by MLE and shows results mle_example.m Example gmm_estimate.m Performs estimation by GMM gmm_results.m Performs estimatiion by GMM and shows results gmm_example.m Example The other functions are mostly for internal use, or for use by advanced users. econometrics/inst/0000755000175000017500000000000012107462002012661 5ustar nirnireconometrics/inst/unscale_parameters.m0000644000175000017500000000230511753175674016742 0ustar nirnir## Copyright (C) 2003, 2004 Michael Creel ## ## 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 3 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 . ## Unscales parameters that were estimated using scaled data ## primarily for use by BFGS function [theta_us, vartheta_us] = unscale_parameters(theta, vartheta, scalecoefs); k = rows(theta); A = scalecoefs {1}; b = scalecoefs {2}; kk = rows(b); B = zeros(kk-1,kk); B = [b'; B]; C = A + B; # allow for parameters that aren't associated with x if (k > kk) D = zeros(kk, k - kk); C = [C, D; D', eye(k - kk)]; endif; theta_us = C*theta; vartheta_us = C * vartheta * C'; endfunction econometrics/inst/mle_example.m0000644000175000017500000000575511753175674015371 0ustar nirnir## Copyright (C) 2003, 2004 Michael Creel ## ## 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 3 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 . ## Example to show how to use MLE functions # Generate data n = 1000; # how many observations? # the explanatory variables: note that they have unequal scales x = [ones(n,1) -rand(n,1) randn(n,1)]; theta = 1:3; # true coefficients are 1,2,3 theta = theta'; lambda = exp(x*theta); y = poissrnd(lambda); # generate the dependent variable #################################### # define arguments for mle_results # #################################### # starting values theta = zeros(3,1); # data data = [y, x]; # name of model to estimate model = "poisson"; modelargs = {0}; # if this is zero the function gives analytic score, otherwise not # parameter names names = char("beta1", "beta2", "beta3"); mletitle = "Poisson MLE trial"; # title for the run # controls for bfgsmin: 30 iterations is not always enough for convergence control = {50,0}; # This displays the results printf("\n\nanalytic score, unscaled data\n"); [theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, mletitle, 0, control); # This just calculates the results, no screen display printf("\n\nanalytic score, unscaled data, no screen display\n"); theta = zeros(3,1); [theta, obj_value, convergence] = mle_estimate(theta, data, model, modelargs, control); printf("obj_value = %f, to confirm it worked\n", obj_value); # This show how to scale data during estimation, but get results # for data in original units (recommended to avoid conditioning problems) # This usually converges faster, depending upon the data printf("\n\nanalytic score, scaled data\n"); [scaled_x, unscale] = scale_data(x); data = [y, scaled_x]; theta = zeros(3,1); [theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, mletitle, unscale, control); # Example using numeric score printf("\n\nnumeric score, scaled data\n"); theta = zeros(3,1); modelargs = {1}; # set the switch for no score [theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, mletitle, unscale, control); # Example doing estimation in parallel on a cluster (requires MPITB) # uncomment the following if you have MPITB installed # theta = zeros(3,1); # nslaves = 1; # title = "MLE estimation done in parallel"; # [theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, mletitle, unscale, control, nslaves); econometrics/inst/kernel_example.m0000644000175000017500000001224311753202146016043 0ustar nirnir## Copyright (C) 2007 Michael Creel ## ## 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 3 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 . ## kernel_example: examples of how to use kernel density and regression functions ## requires the optim and plot packages from Octave Forge ## ## usage: kernel_example; # sample size (default n = 500) - should get better fit (on average) # as this increases, supposing that you allow the optimal window width # to be found, by uncommenting the relevant lines n = 500; # set this to greater than 0 to try parallel computations (requires MPITB) compute_nodes = 0; nodes = compute_nodes + 1; # count master node close all; hold off; ############################################################ # kernel regression example # uniformly spaced data points on [0,2] x = 1:n; x = x'; x = 2*x/n; # generate dependent variable trueline = x + (x.^2)/2 - 3.1*(x.^3)/3 + 1.2*(x.^4)/4; sig = 0.5; y = trueline + sig*randn(n,1); tic; fit = kernel_regression(x, y, x); # use the default bandwidth t1 = toc; printf("\n"); printf("########################################################################\n"); printf("time for kernel regression example using %d data points and %d compute nodes: %f\n", n, nodes, t1); plot(x, fit, ";fit;", x, trueline,";true;"); grid("on"); title("Example 1: Kernel regression fit"); ############################################################ # kernel density example: univariate - fit to Chi^2(3) data data = sumsq(randn(n,3),2); # evaluation point are on a grid for plotting stepsize = 0.2; grid_x = (-1:stepsize:11)'; bandwidth = 0.55; # get optimal bandwidth (time consuming, uncomment if you want to try it) # bandwidth = kernel_optimal_bandwidth(data); # get the fitted density and do a plot tic; dens = kernel_density(grid_x, data, bandwidth, "kernel_normal", false, false, compute_nodes); t1 = toc; printf("\n"); printf("########################################################################\n"); printf("time for univariate kernel density example using %d data points and %d compute nodes: %f\n", n, nodes, t1); printf("A rough integration under the fitted univariate density is %f\n", sum(dens)*stepsize); figure(); plot(grid_x, dens, ";fitted density;", grid_x, chi2pdf(grid_x,3), ";true density;"); title("Example 2: Kernel density fit: Univariate Chi^2(3) data"); ############################################################ # kernel density example: bivariate # X ~ N(0,1) # Y ~ Chi squared(3) # X, Y are dependent d = randn(n,3); data = [d(:,1) sumsq(d,2)]; # evaluation points are on a grid for plotting stepsize = 0.2; a = (-5:stepsize:5)'; # for the N(0,1) b = (-1:stepsize:9)'; # for the Chi squared(3) gridsize = rows(a); [grid_x, grid_y] = meshgrid(a, b); eval_points = [vec(grid_x) vec(grid_y)]; bandwidth = 0.85; # get optimal bandwidth (time consuming, uncomment if you want to try it) # bandwidth = kernel_optimal_bandwidth(data); # get the fitted density and do a plot tic; dens = kernel_density(eval_points, data, bandwidth, "kernel_normal", false, false, compute_nodes); t1 = toc; printf("\n"); printf("########################################################################\n"); printf("time for multivariate kernel density example using %d data points and %d compute nodes: %f\n", n, nodes, t1); dens = reshape(dens, gridsize, gridsize); printf("A rough integration under the fitted bivariate density is %f\n", sum(sum(dens))*stepsize^2); figure(); surf(grid_x, grid_y, dens); title("Example 3: Kernel density fit: dependent bivariate data"); xlabel("true marginal density is N(0,1)"); ylabel("true marginal density is Chi^2(3)"); # more extensive test of parallel if compute_nodes > 0 # only try this if parallel is available ns =[4000; 8000; 10000; 12000; 16000; 20000]; printf("\n"); printf("########################################################################\n"); printf("kernel regression example with several sample sizes serial/parallel timings\n"); figure(); clf; title("Compute time versus nodes, kernel regression with different sample sizes"); xlabel("nodes"); ylabel("time (sec)"); hold on; ts = zeros(rows(ns),4); for i = 1:rows(ns) for compute_nodes = 0:3 nodes = compute_nodes + 1; n = ns(i,:); x = 1:n; x = x'; x = 2*x/n; # generate dependent variable trueline = x + (x.^2)/2 - 3.1*(x.^3)/3 + 1.2*(x.^4)/4; sig = 0.5; y = trueline + sig*randn(n,1); bandwidth = 0.45; tic; fit = kernel_regression(x, y, x, bandwidth, "kernel_normal", false, false, compute_nodes); t1 = toc; ts(i, nodes) = t1; plot(nodes, t1, "*"); printf(" %d data points and %d compute nodes: %f\n", n, nodes, t1); endfor plot(ts(i,:)'); endfor hold off; endif econometrics/inst/delta_method.m0000644000175000017500000000201011753175674015507 0ustar nirnir## Copyright (C) 2003, 2004 Michael Creel ## ## 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 3 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 . ## Computes Delta method mean and covariance of a nonlinear ## transformation defined by "func" function [theta_transf, var_transf] = delta_method(func, theta, otherargs, vartheta) theta_transf = feval(func, theta, otherargs); D = numgradient(func, {theta, otherargs}); var_transf = D * vartheta * D'; endfunction econometrics/inst/parameterize.m0000644000175000017500000000212311753175674015553 0ustar nirnir## Copyright (C) 2003, 2004 Michael Creel ## ## 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 3 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 . ## usage: theta = parameterize(theta, otherargs) ## ## This is an empty function, provided so that ## delta_method will work as is. Replace it with ## the parameter transformations your models use. ## Note: you can let "otherargs" contain the model ## name so that this function can do parameterizations ## for a variety of models function theta = parameterize(theta, otherargs) endfunction econometrics/inst/mle_obj.m0000644000175000017500000000425211753175674014477 0ustar nirnir## Copyright (C) 2003, 2004, 2005 Michael Creel ## ## 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 3 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 . ## usage: [obj_value, score] = mle_obj(theta, data, model, modelargs, nslaves) ## ## Returns the average log-likelihood for a specified model ## This is for internal use by mle_estimate function [obj_value, score] = mle_obj(theta, data, model, modelargs, nslaves = 0) n = rows(data); if nslaves > 0 global NSLAVES PARALLEL NEWORLD NSLAVES TAG; nn = floor(n/(NSLAVES + 1)); # number of obsns per slave # The command that the slave nodes will execute cmd=['contrib = mle_obj_nodes(theta, data, model, modelargs, nn); ',... 'MPI_Send(contrib,0,TAG,NEWORLD);']; # send items to slaves NumCmds_Send({"theta", "nn", "cmd"}, {theta, nn, cmd}); # evaluate last block on master while slaves are busy obj_value = mle_obj_nodes(theta, data, model, modelargs, nn); # collect slaves' results contrib = 0.0; # must be initialized to use MPI_Recv for i = 1:NSLAVES MPI_Recv(contrib,i,TAG,NEWORLD); obj_value = obj_value + contrib; endfor # compute the average obj_value = - obj_value / n; score = "na"; # fix this later to allow analytic score in parallel else # serial version [contribs, score] = feval(model, theta, data, modelargs); obj_value = - mean(contribs); if isnumeric(score) score = - mean(score)'; endif # model passes "na" when score not available endif # let's bullet-proof this in case the model goes nuts if (((abs(obj_value) == Inf)) || (isnan(obj_value))) obj_value = realmax/10; endif endfunction econometrics/inst/poisson.m0000644000175000017500000000207511753175674014563 0ustar nirnir## Copyright (C) 2005 Michael Creel ## ## 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 3 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 . ## Example likelihood function (Poisson for count data) with score function [log_density, score] = poisson(theta, data, otherargs) y = data(:,1); x = data(:,2:columns(data)); lambda = exp(x*theta); log_density = -lambda + y .* (x*theta) - lgamma(y+1); score = diag (y - lambda) * x; if (otherargs{1} == 1) score = "na"; endif # provide analytic score or not? endfunction econometrics/inst/gmm_example.m0000644000175000017500000000445011753175674015363 0ustar nirnir## Copyright (C) 2003, 2004, 2005 Michael Creel ## ## 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 3 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 . ## GMM example file, shows initial consistent estimator, ## estimation of efficient weight, and second round ## efficient estimator n = 1000; k = 5; x = [ones(n,1) randn(n,k-1)]; w = [x, rand(n,1)]; theta_true = ones(k,1); lambda = exp(x*theta_true); y = poissrnd(lambda); [xs, scalecoef] = scale_data(x); # The arguments for gmm_estimate theta = zeros(k,1); data = [y xs w]; weight = eye(columns(w)); moments = "poisson_moments"; momentargs = {k}; # needed to know where x ends and w starts # additional args for gmm_results names = char("theta1", "theta2", "theta3", "theta4", "theta5"); gmmtitle = "Poisson GMM trial"; control = {100,0,1,1}; # initial consistent estimate: only used to get efficient weight matrix, no screen output [theta, obj_value, convergence] = gmm_estimate(theta, data, weight, moments, momentargs, control); # efficient weight matrix # this method is valid when moments are not autocorrelated # the user is reponsible to properly estimate the efficient weight m = feval(moments, theta, data, momentargs); weight = inverse(cov(m)); # second round efficient estimator gmm_results(theta, data, weight, moments, momentargs, names, gmmtitle, scalecoef, control); printf("\nThe true parameter values used to generate the data:\n"); prettyprint(theta_true, names, "value"); # Example doing estimation in parallel on a cluster (requires MPITB) # uncomment the following if you have MPITB installed # nslaves = 1; # theta = zeros(k,1); # nslaves = 1; # title = "GMM estimation done in parallel"; # gmm_results(theta, data, weight, moments, momentargs, names, gmmtitle, scalecoef, control, nslaves); econometrics/inst/private/0000755000175000017500000000000012107462002014333 5ustar nirnireconometrics/inst/private/kernel_normal.m0000644000175000017500000000211411753202146017346 0ustar nirnir## Copyright (C) 2006 Michael Creel ## ## 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 3 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 . ## kernel_normal: this function is for internal use by kernel_density ## and kernel_regression ## ## product normal kernel ## input: PxK matrix - P data points, each of which is in R^K ## output: Px1 vector, input matrix passed though the kernel ## other multivariate kernel functions should follow this convention function z = kernel_normal(z) z = normpdf(z); z = prod(z,2); endfunction econometrics/inst/private/sum_moments_nodes.m0000644000175000017500000000230111753202146020252 0ustar nirnir## Copyright (C) 2005 Michael Creel ## ## 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 3 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 . ## for internal use by gmm_estimate function contrib = sum_moments_nodes(theta, data, moments, momentargs, nn) global NEWORLD NSLAVES # Who am I? [info, rank] = MPI_Comm_rank(NEWORLD); if rank == 0 # Do this if I'm master startblock = NSLAVES*nn + 1; endblock = rows(data); else # this is for the slaves startblock = rank*nn-nn+1; endblock = rank*nn; endif data = data(startblock:endblock,:); contrib = feval(moments, theta, data, momentargs); contrib = sum(contrib); endfunction econometrics/inst/private/kernel_regression_nodes.m0000644000175000017500000000355411753202146021437 0ustar nirnir## Copyright (C) 2006, 2007 Michael Creel ## ## 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 3 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 . ## kernel_regression_nodes: for internal use by kernel_regression - does calculations on nodes function z = kernel_regression_nodes(eval_points, data, do_cv, kernel, points_per_node, nslaves, debug) if (nslaves > 0) global NEWORLD [info, myrank] = MPI_Comm_rank(NEWORLD); else myrank = 0; # if not parallel then do all on master node endif if myrank == 0 # Do this if I'm master startblock = nslaves*points_per_node + 1; endblock = rows(eval_points); else # this is for the slaves startblock = myrank*points_per_node - points_per_node + 1; endblock = myrank*points_per_node; endif # the block of eval_points this node does myeval = eval_points(startblock:endblock,:); nn = rows(myeval); n = rows(data); y = data(:,1); data = data(:,2:columns(data)); W = __kernel_weights(data, myeval, kernel); # drop own weight for CV if (do_cv) W = W - diag(diag(W)); endif den = sum(W,2); if !all(den) warning("kernel_regression: some evaluation points have no neighbors - increase the bandwidth"); den = den + eps; # avoid divide by zero endif W = W ./ (repmat(den,1,n)); z = W*y; if debug printf("z on node %d: \n", myrank); z' endif endfunction econometrics/inst/private/nls_obj_nodes.m0000644000175000017500000000227311753202146017342 0ustar nirnir## Copyright (C) 2005 Michael Creel ## ## 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 3 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 . ## This is for internal use by nls_estimate function contrib = nls_obj_nodes(theta, data, model, modelargs, nn) global NEWORLD NSLAVES # Who am I? [info, rank] = MPI_Comm_rank(NEWORLD); if rank == 0 # Do this if I'm master startblock = NSLAVES*nn + 1; endblock = rows(data); else # this is for the slaves startblock = rank*nn-nn+1; endblock = rank*nn; endif data = data(startblock:endblock,:); contrib = feval(model, theta, data, modelargs); contrib = sum(contrib); endfunction econometrics/inst/private/mle_variance.m0000644000175000017500000000227111753202146017147 0ustar nirnir## Copyright (C) 2003, 2004, 2005 Michael Creel ## ## 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 3 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 . ## usage: [V,scorecontribs,J_inv] = ## mle_variance(theta, data, model, modelargs) ## ## This is for internal use by mle_results # sandwich form of var-cov matrix function [V, scorecontribs, J_inv] = mle_variance(theta, data, model, modelargs) scorecontribs = numgradient(model, {theta, data, modelargs}); n = rows(scorecontribs); I = scorecontribs'*scorecontribs / n; J = numhessian("mle_obj", {theta, data, model, modelargs}); J_inv = inverse(J); V = J_inv*I*J_inv/n; endfunction econometrics/inst/private/kernel_density_nodes.m0000644000175000017500000000316011753202146020727 0ustar nirnir## Copyright (C) 2006, 2007 Michael Creel ## ## 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 3 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 . ## kernel_density_nodes: for internal use by kernel_density - does calculations on nodes function z = kernel_density_nodes(eval_points, data, do_cv, kernel, points_per_node, nslaves, debug) if (nslaves > 0) global NEWORLD [info, myrank] = MPI_Comm_rank(NEWORLD); else myrank = 0; # if not parallel then do all on master node endif if myrank == 0 # Do this if I'm master startblock = nslaves*points_per_node + 1; endblock = rows(eval_points); else # this is for the slaves startblock = myrank*points_per_node - points_per_node + 1; endblock = myrank*points_per_node; endif # the block of eval_points this node does myeval = eval_points(startblock:endblock,:); nn = rows(myeval); n = rows(data); W = __kernel_weights(data, myeval, kernel); if (do_cv) W = W - diag(diag(W)); z = sum(W,2) / (n-1); else z = sum(W,2) / n; endif if debug printf("z on node %d: \n", myrank); z' endif endfunction econometrics/inst/private/kernel_epanechnikov.m0000644000175000017500000000237711753202146020543 0ustar nirnir## Copyright (C) 2006 Michael Creel ## ## 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 3 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 . ## kernel_epanechnikov: this function is for internal use by kernel_density ## and kernel_regression ## ## multivariate spherical Epanechnikov kernel ## input: PxK matrix - P data points, each of which is in R^K ## output: Px1 vector, input matrix passed though the kernel ## other multivariate kernel functions should follow this convention function z = kernel_epanechnikov(z) K = columns(z); # Volume of d-dimensional unit sphere c = pi ^ (K/2) / gamma(K/2 + 1); # compute kernel z = sumsq(z, 2); z = ((1/2) / c * (K + 2) * (1 - z)) .* (z < 1); endfunction econometrics/inst/private/average_moments.m0000644000175000017500000000342211753202146017675 0ustar nirnir## Copyright (C) 2003, 2004, 2005 Michael Creel ## ## 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 3 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 . ## for internal use by gmm_estimate ## average moments (separate function so it can be differentiated) function m = average_moments(theta, data, moments, momentargs) global NSLAVES PARALLEL NEWORLD NSLAVES TAG; n = rows(data); if PARALLEL nn = floor(n/(NSLAVES + 1)); # save some work for master # The command that the slave nodes will execute cmd=['contrib = sum_moments_nodes(theta, data, moments, momentargs, nn); ',... 'MPI_Send(contrib,0,TAG,NEWORLD);']; # send items to slaves NumCmds_Send({"theta", "nn", "cmd"},{theta, nn, cmd}); # evaluate last block on master while slaves are busy m = feval("sum_moments_nodes", theta, data, moments, momentargs, nn); # collect slaves' results contrib = zeros(1,columns(m)); for i = 1:NSLAVES MPI_Recv(contrib,i,TAG,NEWORLD); m = m + contrib; endfor m = m'; # we want a column vector, please m = m/n; # average please, not sum else # serial version m = feval(moments, theta, data, momentargs); m = mean(m)'; # returns Gx1 moment vector endif endfunction econometrics/inst/nls_estimate.m0000644000175000017500000000541411753175674015560 0ustar nirnir## Copyright (C) 2005 Michael Creel ## ## 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 3 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 . ## usage: ## [theta, obj_value, conv, iters] = nls_estimate(theta, data, model, modelargs, control, nslaves) ## ## inputs: ## theta: column vector of model parameters ## data: data matrix ## model: name of function that computes the vector of sums of squared errors ## modelargs: (cell) additional inputs needed by model. May be empty ("") ## 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: NLS estimated value of parameters ## obj_value: the value of the sum of squared errors at NLS estimate ## conv: return code from bfgsmin (1 means success, see bfgsmin for details) ## iters: number of BFGS iteration used ## ## please see nls_example.m for examples of how to use this function [theta, obj_value, convergence, iters] = nls_estimate(theta, data, model, modelargs, control, nslaves) if nargin < 3 error("nls_estimate: 3 arguments required"); endif if nargin < 4 modelargs = {}; endif # create placeholder if not used if !iscell(modelargs) modelargs = {}; endif # default controls if receive placeholder if nargin < 5 control = {Inf,0,1,1}; endif # default controls and method if !iscell(control) control = {Inf,0,1,1}; endif # default controls if receive placeholder if nargin < 6 nslaves = 0; endif if nslaves > 0 global NSLAVES PARALLEL NEWORLD TAG LAM_Init(nslaves); # Send the data to all nodes NumCmds_Send({"data", "model", "modelargs"}, {data, model, modelargs}); endif # bfgs or sa? if (size(control,1)*size(control,2) == 0) # use default bfgs if no control control = {Inf,0,1,1}; method = "bfgs"; elseif (size(control,1)*size(control,2) < 11) method = "bfgs"; else method = "sa"; endif if strcmp(method, "bfgs") [theta, obj_value, convergence, iters] = bfgsmin("nls_obj", {theta, data, model, modelargs, nslaves}, control); elseif strcmp(method, "sa") [theta, obj_value, convergence] = samin("nls_obj", {theta, data, model, modelargs, nslaves}, control); endif # cleanup if nslaves > 0 LAM_Finalize; endif endfunction econometrics/inst/nls_obj.m0000644000175000017500000000417711753175674014524 0ustar nirnir## Copyright (C) 2005 Michael Creel ## ## 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 3 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 . ## usage: [obj_value, score] = nls_obj(theta, data, model, modelargs, nslaves) ## ## Returns the average sum of squared errors for a specified model ## This is for internal use by nls_estimate function [obj_value, score] = nls_obj(theta, data, model, modelargs, nslaves) n = rows(data); if nslaves > 0 global NEWORLD NSLAVES TAG nn = floor(n/(NSLAVES + 1)); # number of obsns per slave # The command that the slave nodes will execute cmd=['contrib = nls_obj_nodes(theta, data, model, modelargs, nn); ',... 'MPI_Send(contrib,0,TAG,NEWORLD);']; # send items to slaves NumCmds_Send({"theta", "nn", "cmd"}, {theta, nn, cmd}); # evaluate last block on master while slaves are busy obj_value = nls_obj_nodes(theta, data, model, modelargs, nn); # collect slaves' results contrib = 0.0; # must be initialized to use MPI_Recv for i = 1:NSLAVES MPI_Recv(contrib,i,TAG,NEWORLD); obj_value = obj_value + contrib; endfor # compute the average obj_value = obj_value / n; score = "na"; # fix this later to allow analytic score in parallel else # serial version [contribs, score] = feval(model, theta, data, modelargs); obj_value = mean(contribs); if isnumeric(score) score = mean(score)'; endif # model passes "na" when score not available endif # let's bullet-proof this in case the model goes nuts if (((abs(obj_value) == Inf)) || (isnan(obj_value))) obj_value = realmax; endif endfunction econometrics/inst/mle_results.m0000644000175000017500000000711111753175674015423 0ustar nirnir## Copyright (C) 2003, 2004, 2005 Michael Creel ## ## 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 3 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 . ## 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 econometrics/inst/kernel_density.m0000644000175000017500000001113711753202146016070 0ustar nirnir## Copyright (C) 2006 Michael Creel ## ## 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 3 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 . ## kernel_density: multivariate kernel density estimator ## ## usage: ## dens = kernel_density(eval_points, data, bandwidth) ## ## inputs: ## eval_points: PxK matrix of points at which to calculate the density ## data: NxK matrix of data points ## bandwidth: positive scalar, the smoothing parameter. The fit ## is more smooth as the bandwidth increases. ## kernel (optional): string. Name of the kernel function. Default is ## Gaussian kernel. ## prewhiten bool (optional): default false. If true, rotate data ## using Choleski decomposition of inverse of covariance, ## to approximate independence after the transformation, which ## makes a product kernel a reasonable choice. ## do_cv: bool (optional). default false. If true, calculate leave-1-out ## density for cross validation ## computenodes: int (optional, default 0). ## Number of compute nodes for parallel evaluation ## debug: bool (optional, default false). show results on compute nodes if doing ## a parallel run ## outputs: ## dens: Px1 vector: the fitted density value at each of the P evaluation points. ## ## References: ## Wand, M.P. and Jones, M.C. (1995), 'Kernel smoothing'. ## http://www.xplore-stat.de/ebooks/scripts/spm/html/spmhtmlframe73.html function z = kernel_density(eval_points, data, bandwidth, kernel, prewhiten, do_cv, computenodes, debug) if nargin < 2; error("kernel_density: at least 2 arguments are required"); endif n = rows(data); k = columns(data); # set defaults for optional args if (nargin < 3) bandwidth = (n ^ (-1/(4+k))); endif # bandwidth - see Li and Racine pg. 26 if (nargin < 4) kernel = "kernel_normal"; endif # what kernel? if (nargin < 5) prewhiten = false; endif # automatic prewhitening? if (nargin < 6) do_cv = false; endif # ordinary or leave-1-out if (nargin < 7) computenodes = 0; endif # parallel? if (nargin < 8) debug = false; endif; # debug? nn = rows(eval_points); n = rows(data); if prewhiten H = bandwidth*chol(cov(data)); else H = bandwidth; endif # Inverse bandwidth matrix H_inv H_inv = inv(H); # weight by inverse bandwidth matrix eval_points = eval_points*H_inv; data = data*H_inv; # check if doing this parallel or serial global PARALLEL NSLAVES NEWORLD NSLAVES TAG PARALLEL = 0; if computenodes > 0 PARALLEL = 1; NSLAVES = computenodes; LAM_Init(computenodes, debug); endif if !PARALLEL # ordinary serial version points_per_node = nn; # do the all on this node z = kernel_density_nodes(eval_points, data, do_cv, kernel, points_per_node, computenodes, debug); else # parallel version z = zeros(nn,1); points_per_node = floor(nn/(NSLAVES + 1)); # number of obsns per slave # The command that the slave nodes will execute cmd=['z_on_node = kernel_density_nodes(eval_points, data, do_cv, kernel, points_per_node, computenodes, debug); ',... 'MPI_Send(z_on_node, 0, TAG, NEWORLD);']; # send items to slaves NumCmds_Send({"eval_points", "data", "do_cv", "kernel", "points_per_node", "computenodes", "debug","cmd"}, {eval_points, data, do_cv, kernel, points_per_node, computenodes, debug, cmd}); # evaluate last block on master while slaves are busy z_on_node = kernel_density_nodes(eval_points, data, do_cv, kernel, points_per_node, computenodes, debug); startblock = NSLAVES*points_per_node + 1; endblock = nn; z(startblock:endblock,:) = z(startblock:endblock,:) + z_on_node; # collect slaves' results z_on_node = zeros(points_per_node,1); # size may differ between master and compute nodes - reset here for i = 1:NSLAVES MPI_Recv(z_on_node,i,TAG,NEWORLD); startblock = i*points_per_node - points_per_node + 1; endblock = i*points_per_node; z(startblock:endblock,:) = z(startblock:endblock,:) + z_on_node; endfor # clean up after parallel LAM_Finalize; endif z = z*det(H_inv); endfunction econometrics/inst/kernel_regression_cvscore.m0000644000175000017500000000167211753175674020337 0ustar nirnir## Copyright (C) 2006 Michael Creel ## ## 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 3 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 . ## cvscore = kernel_regression_cvscore(bandwidth, data, depvar) function cvscore = kernel_regression_cvscore(bandwidth, data, depvar) fit = kernel_regression(data, depvar, data, exp(bandwidth), true); cvscore = norm(depvar - fit); endfunction econometrics/inst/mle_estimate.m0000644000175000017500000000554211753175674015543 0ustar nirnir## Copyright (C) 2003, 2004, 2005 Michael Creel ## ## 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 3 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 . ## usage: ## [theta, obj_value, conv, iters] = mle_estimate(theta, data, model, modelargs, control, nslaves) ## ## 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 ("") ## 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.m for examples of how to use this function [theta, obj_value, convergence, iters] = mle_estimate(theta, data, model, modelargs, control, nslaves = 0) if nargin < 3 error("mle_estimate: 3 arguments required"); endif if nargin < 4 modelargs = {}; endif # create placeholder if not used if !iscell(modelargs) modelargs = {}; endif # default controls if receive placeholder if nargin < 5 control = {-1,0,1,1}; endif # default controls and method if !iscell(control) control = {-1,0,1,1}; endif # default controls if receive placeholder if nslaves > 0 global NSLAVES PARALLEL NEWORLD TAG; LAM_Init(nslaves); # Send the data to all nodes NumCmds_Send({"data", "model", "modelargs"}, {data, model, modelargs}); endif # bfgs or sa? if (size(control,1)*size(control,2) == 0) # use default bfgs if no control control = {Inf,0,1,1}; method = "bfgs"; elseif (size(control,1)*size(control,2) < 11) method = "bfgs"; else method = "sa"; endif # do estimation using either bfgsmin or samin if strcmp(method, "bfgs") [theta, obj_value, convergence, iters] = bfgsmin("mle_obj", {theta, data, model, modelargs, nslaves}, control); elseif strcmp(method, "sa") [theta, obj_value, convergence] = samin("mle_obj", {theta, data, model, modelargs, nslaves}, control); endif if nslaves > 0 LAM_Finalize; endif # cleanup obj_value = - obj_value; # recover from minimization rather than maximization endfunction econometrics/inst/prettyprint_c.m0000644000175000017500000000222711753175674015776 0ustar nirnir## Copyright (C) 2003, 2004 Michael Creel ## ## 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 3 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 . ## this prints matrices with column labels but no row labels function prettyprint_c(mat, clabels) printf(" "); # print the column labels clabels = [" ";clabels]; # pad to 8 characters wide clabels = strjust(clabels,"right"); k = columns(mat); for i = 1:k printf("%s ",clabels(i+1,:)); endfor # now print the row labels and rows printf("\n"); k = rows(mat); for i = 1:k printf(" %8.3f", mat(i,:)); printf("\n"); endfor endfunction econometrics/inst/scale_data.m0000644000175000017500000000255211753175674015151 0ustar nirnir## Copyright (C) 2003, 2004 Michael Creel ## ## 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 3 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 . ## Standardizes and normalizes data matrix, ## primarily for use by BFGS function [zz, scalecoefs] = scale_data(z); n = rows(z); k = columns(z); # Scale data s = std(z)'; test = s != 0; s = s + (1 - test); # don't scale if column is a constant (avoid div by zero) A = diag(1 ./ s); # De-mean all variables except constant, if a constant is present test = std(z(:,1)) != 0; if test bb = zeros(n,k); b = zeros(k,1); else b = -mean(z)'; test = std(z)' != 0; # don't take out mean if the column is a constant, to preserve identification b = b .* test; b = A*b; bb = (diag(b) * ones(k,n))'; endif zz = z*A + bb; scalecoefs = {A,b}; endfunction econometrics/inst/prettyprint.m0000644000175000017500000000252311753175674015473 0ustar nirnir## Copyright (C) 2003, 2004 Michael Creel ## ## 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 3 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 . ## this prints matrices with row and column labels function prettyprint(mat, rlabels, clabels) # left pad the column labels a = columns(rlabels); for i = 1:a printf(" "); endfor printf(" "); # print the column labels clabels = [" ";clabels]; # pad to 8 characters wide clabels = strjust(clabels,"right"); k = columns(mat); for i = 1:k printf("%s ",clabels(i+1,:)); endfor # now print the row labels and rows printf("\n"); k = rows(mat); for i = 1:k if ischar(rlabels(i,:)) printf(rlabels(i,:)); else printf("%i", rlabels(i,:)); endif printf(" %10.3f", mat(i,:)); printf("\n"); endfor endfunction econometrics/inst/nls_example.m0000644000175000017500000000352411753175674015400 0ustar nirnir## Copyright (C) 2006 Michael Creel ## ## 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 3 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 . ## Example to show how to use NLS # Generate data n = 100; # how many observations? # the explanatory variables: note that they have unequal scales x = [ones(n,1) rand(n,2)]; theta = 1:3; # true coefficients are 1,2,3 theta = theta'; lambda = exp(x*theta); y = randp(lambda); # generate the dependent variable # example objective function for nls function [obj_contrib, score] = nls_example_obj(theta, data, otherargs) y = data(:,1); x = data(:,2:columns(data)); lambda = exp(x*theta); errors = y - lambda; obj_contrib = errors .* errors; score = "na"; endfunction ##################################### # define arguments for nls_estimate # ##################################### # starting values theta = zeros(3,1); # data data = [y, x]; # name of model to estimate model = "nls_example_obj"; modelargs = {}; # none required for this obj fn. # controls for bfgsmin - limit to 50 iters, and print final results control = {50,1}; #################################### # do the estimation # #################################### printf("\nNLS estimation example\n"); [theta, obj_value, convergence] = nls_estimate(theta, data, model, modelargs, control); econometrics/inst/gmm_variance.m0000644000175000017500000000203311753175674015513 0ustar nirnir## Copyright (C) 2003, 2004 Michael Creel ## ## 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 3 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 . ## GMM variance, which assumes weights are optimal function V = gmm_variance(theta, data, weight, moments, momentargs) D = numgradient("average_moments", {theta, data, moments, momentargs}); D = D'; m = feval(moments, theta, data, momentargs); # find out how many obsns. we have n = rows(m); V = (1/n)*inv(D*weight*D'); endfunction econometrics/inst/poisson_moments.m0000644000175000017500000000201611753175674016320 0ustar nirnir## Copyright (C) 2005 Michael Creel ## ## 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 3 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 . ## the form a user-written moment function should take function m = poisson_moments(theta, data, momentargs) k = momentargs{1}; # use this so that data can hold dep, indeps, and instr y = data(:,1); x = data(:,2:k+1); w = data(:, k+2:columns(data)); lambda = exp(x*theta); e = y ./ lambda - 1; m = diag(e) * w; endfunction econometrics/inst/gmm_estimate.m0000644000175000017500000000551611753175674015547 0ustar nirnir## Copyright (C) 2003, 2004, 2005 Michael Creel ## ## 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 3 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 . ## usage: [theta, obj_value, convergence, iters] = ## gmm_estimate(theta, data, weight, moments, momentargs, control, nslaves) ## ## inputs: ## theta: column vector initial parameters ## data: data matrix ## weight: the GMM weight matrix ## moments: name of function computes the moments ## (should return nXg matrix of contributions) ## momentargs: (cell) additional inputs needed to compute moments. ## May be empty ("") ## 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: GMM estimate of parameters ## obj_value: the value of the gmm obj. function ## convergence: return code from bfgsmin ## (1 means success, see bfgsmin for details) ## iters: number of BFGS iteration used ## ## please type "gmm_example" while in octave to see an example ## call the minimizing routine function [theta, obj_value, convergence, iters] = gmm_estimate(theta, data, weight, moments, momentargs, control, nslaves) if nargin < 5 error("gmm_estimate: 5 arguments required"); endif if nargin < 6 control = {-1}; endif # default controls if !iscell(control) control = {-1}; endif # default controls if receive placeholder if nargin < 7 nslaves = 0; endif if nslaves > 0 global NSLAVES PARALLEL NEWORLD NSLAVES TAG; LAM_Init(nslaves); # Send the data to all nodes NumCmds_Send({"data", "weight", "moments", "momentargs"}, {data, weight, moments, momentargs}); endif # bfgs or sa? if (size(control,1)*size(control,2) == 0) # use default bfgs if no control control = {Inf,0,1,1}; method = "bfgs"; elseif (size(control,1)*size(control,2) < 11) method = "bfgs"; else method = "sa"; endif if strcmp(method, "bfgs") [theta, obj_value, convergence, iters] = bfgsmin("gmm_obj", {theta, data, weight, moments, momentargs}, control); elseif strcmp(method, "sa") [theta, obj_value, convergence] = samin("gmm_obj", {theta, data, weight, moments, momentargs}, control); endif if nslaves > 0 LAM_Finalize; endif # clean up endfunction econometrics/inst/kernel_regression.m0000644000175000017500000001110611753202146016565 0ustar nirnir## Copyright (C) 2006 Michael Creel ## ## 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 3 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 . ## kernel_regression: kernel regression estimator ## ## usage: ## fit = kernel_regression(eval_points, depvar, condvars, bandwidth) ## ## inputs: ## eval_points: PxK matrix of points at which to calculate the density ## depvar: Nx1 vector of observations of the dependent variable ## condvars: NxK matrix of data points ## bandwidth (optional): positive scalar, the smoothing parameter. ## Default is N ^ (-1/(4+K)) ## kernel (optional): string. Name of the kernel function. Default is ## Gaussian kernel. ## prewhiten bool (optional): default true. If true, rotate data ## using Choleski decomposition of inverse of covariance, ## to approximate independence after the transformation, which ## makes a product kernel a reasonable choice. ## do_cv: bool (optional). default false. If true, calculate leave-1-out ## fit to calculate the cross validation score ## computenodes: int (optional, default 0). ## Number of compute nodes for parallel evaluation ## debug: bool (optional, default false). show results on compute nodes if doing ## a parallel run ## outputs: ## fit: Px1 vector: the fitted value at each of the P evaluation points. function z = kernel_regression(eval_points, depvar, condvars, bandwidth, kernel, prewhiten, do_cv, computenodes, debug) if nargin < 3; error("kernel_regression: at least 3 arguments are required"); endif n = rows(condvars); k = columns(condvars); # set defaults for optional args if (nargin < 4) bandwidth = (n ^ (-1/(4+k))); endif # bandwidth - see Li and Racine pg. 66 if (nargin < 5) kernel = "kernel_normal"; endif # what kernel? if (nargin < 6) prewhiten = true; endif # automatic prewhitening? if (nargin < 7) do_cv = false; endif # ordinary or leave-1-out if (nargin < 8) computenodes = 0; endif # parallel? if (nargin < 9) debug = false; endif; # debug? nn = rows(eval_points); n = rows(depvar); if prewhiten H = bandwidth*chol(cov(condvars)); else H = bandwidth; endif H_inv = inv(H); # weight by inverse bandwidth matrix eval_points = eval_points*H_inv; condvars = condvars*H_inv; data = [depvar condvars]; # put it all together for sending to nodes # check if doing this parallel or serial global PARALLEL NSLAVES NEWORLD NSLAVES TAG PARALLEL = 0; if computenodes > 0 PARALLEL = 1; NSLAVES = computenodes; LAM_Init(computenodes, debug); endif if !PARALLEL # ordinary serial version points_per_node = nn; # do the all on this node z = kernel_regression_nodes(eval_points, data, do_cv, kernel, points_per_node, computenodes, debug); else # parallel version z = zeros(nn,1); points_per_node = floor(nn/(NSLAVES + 1)); # number of obsns per slave # The command that the slave nodes will execute cmd=['z_on_node = kernel_regression_nodes(eval_points, data, do_cv, kernel, points_per_node, computenodes, debug); ',... 'MPI_Send(z_on_node, 0, TAG, NEWORLD);']; # send items to slaves NumCmds_Send({"eval_points", "data", "do_cv", "kernel", "points_per_node", "computenodes", "debug","cmd"}, {eval_points, data, do_cv, kernel, points_per_node, computenodes, debug, cmd}); # evaluate last block on master while slaves are busy z_on_node = kernel_regression_nodes(eval_points, data, do_cv, kernel, points_per_node, computenodes, debug); startblock = NSLAVES*points_per_node + 1; endblock = nn; z(startblock:endblock,:) = z(startblock:endblock,:) + z_on_node; # collect slaves' results z_on_node = zeros(points_per_node,1); # size may differ between master and compute nodes - reset here for i = 1:NSLAVES MPI_Recv(z_on_node,i,TAG,NEWORLD); startblock = i*points_per_node - points_per_node + 1; endblock = i*points_per_node; z(startblock:endblock,:) = z(startblock:endblock,:) + z_on_node; endfor # clean up after parallel LAM_Finalize; endif endfunction econometrics/inst/kernel_optimal_bandwidth.m0000644000175000017500000000377711753202146020115 0ustar nirnir## Copyright (C) 2007 Michael Creel ## ## 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 3 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 . ## kernel_optimal_bandwidth: find optimal bandwith doing leave-one-out cross validation ## inputs: ## * data: data matrix ## * depvar: column vector or empty (""). ## If empty, do kernel density, orherwise, kernel regression ## * kernel (optional, string) the kernel function to use ## output: ## * h: the optimal bandwidth function bandwidth = kernel_optimal_bandwidth(data, depvar, kernel) if (nargin < 2) error("kernel_optimal_bandwidth: 3 arguments required"); endif if (nargin < 3) kernel = "kernel_epanechnikov"; endif do_density = false; if isempty(depvar) do_density = true; endif; # SA controls ub = 3; lb = -5; nt = 1; ns = 1; rt = 0.05; maxevals = 50; neps = 5; functol = 1e-2; paramtol = 1e-3; verbosity = 0; minarg = 1; sa_control = { lb, ub, nt, ns, rt, maxevals, neps, functol, paramtol, verbosity, 1}; # bfgs controls bfgs_control = {10}; if do_density bandwidth = samin("kernel_density_cvscore", {1, data, kernel}, sa_control); bandwidth = bfgsmin("kernel_density_cvscore", {bandwidth, data, kernel}, bfgs_control); else bandwidth = samin("kernel_regression_cvscore", {1, data, depvar, kernel}, sa_control); bandwidth = bfgsmin("kernel_regression_cvscore", {bandwidth, data, depvar, kernel}, bfgs_control); endif bandwidth = exp(bandwidth); endfunction econometrics/inst/gmm_variance_inefficient.m0000644000175000017500000000203111753175674020054 0ustar nirnir## Copyright (C) 2003, 2004 Michael Creel ## ## 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 3 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 . ## GMM variance, which assumes weights are not optimal function V = gmm_variance_inefficient(theta, data, weight, omega, moments, momentargs) D = numgradient("average_moments", {theta, data, moments, momentargs}); D = D'; n = rows(data); J = D*weight*D'; J = inv(J); I = D*weight*omega*weight*D'; V = (1/n)*J*I*J; endfunction econometrics/inst/gmm_results.m0000644000175000017500000000725211753175674015434 0ustar nirnir## Copyright (C) 2003, 2004, 2005 Michael Creel ## ## 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 3 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 . ## usage: [theta, V, obj_value] = ## gmm_results(theta, data, weight, moments, momentargs, names, title, unscale, control, nslaves) ## ## inputs: ## theta: column vector initial parameters ## data: data matrix ## weight: the GMM weight matrix ## moments: name of function computes the moments ## (should return nXg matrix of contributions) ## momentargs: (cell) additional inputs needed to compute moments. ## May be empty ("") ## names: vector of parameter names ## e.g., 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: GMM estimated parameters ## V: estimate of covariance of parameters. Assumes the weight matrix ## is optimal (inverse of covariance of moments) ## obj_value: the value of the GMM objective function ## ## please type "gmm_example" while in octave to see an example function [theta, V, obj_value] = gmm_results(theta, data, weight, moments, momentargs, names, title, unscale, control, nslaves) if nargin < 10 nslaves = 0; endif # serial by default if nargin < 9 [theta, obj_value, convergence] = gmm_estimate(theta, data, weight, moments, momentargs, "", nslaves); else [theta, obj_value, convergence] = gmm_estimate(theta, data, weight, moments, momentargs, control, nslaves); endif m = feval(moments, theta, data, momentargs); # find out how many obsns. we have n = rows(m); if convergence == 1 convergence="Normal convergence"; else convergence="No convergence"; endif V = gmm_variance(theta, data, weight, moments, momentargs); # unscale results if argument has been passed # this puts coefficients into scale corresponding to the original data if nargin > 7 if iscell(unscale) [theta, V] = unscale_parameters(theta, V, unscale); endif endif [theta, V] = delta_method("parameterize", theta, {data, moments, momentargs}, V); k = rows(theta); se = sqrt(diag(V)); printf("\n\n******************************************************\n"); disp(title); printf("\nGMM Estimation Results\n"); printf("BFGS convergence: %s\n", convergence); printf("\nObjective function value: %f\n", obj_value); printf("Observations: %d\n", n); junk = "X^2 test"; df = n - k; if df > 0 clabels = char("Value","df","p-value"); a = [n*obj_value, df, 1 - chi2cdf(n*obj_value, df)]; printf("\n"); prettyprint(a, junk, clabels); else disp("\nExactly identified, no spec. test"); end; # results for parameters a =[theta, se, theta./se, 2 - 2*normcdf(abs(theta ./ se))]; clabels = char("estimate", "st. err", "t-stat", "p-value"); printf("\n"); prettyprint(a, names, clabels); printf("******************************************************\n"); endfunction econometrics/inst/gmm_obj.m0000644000175000017500000000225112107460613014457 0ustar nirnir## Copyright (C) 2003, 2004, 2005 Michael Creel ## ## 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 3 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 . ## The GMM objective function, for internal use by gmm_estimate ## This is scaled so that it converges to a finite number. ## To get the chi-square specification ## test you need to multiply by n (the sample size) function obj_value = gmm_obj(theta, data, weight, moments, momentargs) m = average_moments(theta, data, moments, momentargs); obj_value = m' * weight *m; if (((abs(obj_value) == Inf)) || (isnan(obj_value))) obj_value = realmax; endif endfunction econometrics/inst/mle_obj_nodes.m0000644000175000017500000000232711753175674015670 0ustar nirnir## Copyright (C) 2003, 2004, 2005 Michael Creel ## ## 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 3 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 . ## contrib = mle_obj_nodes(theta, data, model, modelargs, nn) function contrib = mle_obj_nodes(theta, data, model, modelargs, nn) global NEWORLD NSLAVES # Who am I? [info, rank] = MPI_Comm_rank(NEWORLD); if rank == 0 # Do this if I'm master startblock = NSLAVES*nn + 1; endblock = rows(data); else # this is for the slaves startblock = rank*nn-nn+1; endblock = rank*nn; endif data = data(startblock:endblock,:); contrib = feval(model, theta, data, modelargs); contrib = sum(contrib); endfunction econometrics/inst/kernel_density_cvscore.m0000644000175000017500000000200511753175674017625 0ustar nirnir## Copyright (C) 2006 Michael Creel ## ## 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 3 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 . ## cvscore = kernel_density_cvscore(bandwidth, data, kernel) function cvscore = kernel_density_cvscore(bandwidth, data, kernel) dens = kernel_density(data, data, exp(bandwidth), true, 0, 0, chol(cov(data)), kernel); dens = dens + eps; # some kernels can assign zero density cvscore = -mean(log(dens)); endfunction econometrics/DESCRIPTION0000644000175000017500000000051512110174610016250 0ustar carandraugcarandraugName: Econometrics Version: 1.1.1 Date: 2013-02-17 Author: Michael Creel Maintainer: Nir Krakauer Title: Econometrics. Description: Econometrics functions including MLE and GMM based techniques. Depends: octave (>= 2.9.7), optim Autoload: no License: GPLv3+ Url: http://octave.sf.net