269 lines
11 KiB
Matlab
269 lines
11 KiB
Matlab
function nls(eqname, params, data, range, optimizer, varargin)
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% Estimates the parameters of a PAC equation by Nonlinear Least Squares.
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%
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% INPUTS
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% - eqname [string] Name of the pac equation.
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% - params [struct] Describes the parameters to be estimated.
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% - data [dseries] Database for the estimation
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% - range [dates] Range of dates for the estimation.
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% - optimizer [string] Set the optimization algorithm. Possible values
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% are 'fmincon', 'fminunc', 'csminwel',
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% 'fminsearch', 'simplex' and
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% 'annealing'. Default is 'csminwel'.
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% - varargin List of key/value pairs, each key must be a
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% string and values can be strings or real numbers.
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%
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% OUTPUTS
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% - none
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%
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% REMARKS
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% [1] The estimation results are printed in the command line window, and the
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% parameters are updated accordingly in M_.params.
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% [2] The second input is a structure. Each fieldname corresponds to the
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% name of an estimated parameter, the value of the field is the initial
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% guess used for the estimation (by NLS).
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% [3] The third input is a dseries object which must at least contain all
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% the variables appearing in the estimated equation. The residual of the
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% equation must have NaN values in the object.
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% [4] It is assumed that the residual is additive.
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% [5] If the used optimization routines handles inequalities over the
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% estimated parameters, we impose the positivity of the error correction parameter.
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%
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% EXAMPLE
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%
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% pac.estimate.nls('zpac', eparams, dbase, dates('2003Q1'):dates('2016Q3'),'fminunc', 'MaxIter', 50);
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%
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% where zpac is the name of the PAC equation, eparams is a structure
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% containing the guess values for the estimated parameters (in each field),
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% dbase is a dseries object containing the data,
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% dates('2003Q1'):dates('2016Q3') is the range of the sample used for
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% estimation, 'fminunc' is the name of the optimization algorithm (this one
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% is available only if the matylab optimization toolbox is installed), the
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% remaining inputs are the options (key/value) passed to the optimizers.
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% Copyright © 2018-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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global M_ oo_ options_
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is_gauss_newton = false;
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is_lsqnonlin = false;
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if nargin>4 && (isequal(optimizer, 'GaussNewton') || isequal(optimizer, 'lsqnonlin'))
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switch optimizer
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case 'GaussNewton'
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is_gauss_newton = true;
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case 'lsqnonlin'
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is_lsqnonlin = true;
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end
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end
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[pacmodl, lhs, rhs, pnames, enames, xnames, ~, pid, eid, xid, ~, ipnames_, params, data, islaggedvariables] = ...
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pac.estimate.init(M_, oo_, eqname, params, data, range);
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dLHS = M_.aux_vars(strmatch(lhs,M_.endo_names, 'exact')==[M_.aux_vars(:).endo_index]).orig_expr;
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% Check that the error correction term is correct.
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if M_.pac.(pacmodl).ec.istarget(2)
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error(['\nThe error correction term in PAC equation (%s) is not correct.\nThe ' ...
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'error correction term should be the difference between a trend\n' ...
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'and the level of the endogenous variable.'], pacmodl);
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end
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%
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% Rewrite and print the equation.
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%
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[rhs, lhs] = rewrite_equation_with_tables(rhs, lhs, islaggedvariables, pnames, enames, xnames, pid, eid, xid, data);
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% Create a routine for evaluating the residuals of the nonlinear model
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write_residuals_routine(lhs, rhs, eqname, ipnames_, M_, pacmodl);
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% Create a routine for evaluating the sum of squared residuals of the nonlinear model
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write_ssr_routine(lhs, rhs, eqname, ipnames_, M_, pacmodl);
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% Copy (sub)sample data in a matrix.
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DATA = data([range(1)-1, range]).data;
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% Create a function handle returning the sum of square residuals for a given vector of parameters.
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ssrfun = @(p) feval([M_.fname '.ssr_' eqname], p, DATA, M_, oo_);
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% Create a function handle returning the sum of square residuals for a given vector of parameters.
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resfun = @(p) feval([M_.fname '.r_' eqname], p, DATA, M_, oo_);
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% Set initial condition.
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params0 = cell2mat(struct2cell(params));
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% Set defaults for optimizers
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bounds = [];
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parameter_names = [];
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% Set optimizer routine.
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if nargin<5 || isempty(optimizer)
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% Use csminwel by default.
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minalgo = 4;
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else
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switch optimizer
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case 'GaussNewton'
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% Nothing to do here.
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case 'lsqnonlin'
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bounds = ones(length(params0),1)*[-Inf,Inf];
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bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).ec.params)),1) = 0.0;
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bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).ec.params)),2) = 1.0;
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case 'fmincon'
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if isoctave && ~user_has_octave_forge_package('optim', '1.6')
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error('Optimization algorithm ''fmincon'' requires the optim package, version 1.6 or higher')
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elseif ~isoctave && ~user_has_matlab_license('optimization_toolbox')
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error('Optimization algorithm ''fmincon'' requires the Optimization Toolbox')
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end
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minalgo = 1;
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bounds = ones(length(params0),1)*[-Inf,Inf];
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bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).ec.params)),1) = 0.0;
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bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).ec.params)),2) = 1.0;
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case 'fminunc'
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if isoctave && ~user_has_octave_forge_package('optim')
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error('Optimization algorithm ''fminunc'' requires the optim package')
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elseif ~isoctave && ~user_has_matlab_license('optimization_toolbox')
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error('Optimization algorithm ''fminunc'' requires the Optimization Toolbox')
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end
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minalgo = 3;
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case 'csminwel'
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minalgo = 4;
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case 'fminsearch'
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if isoctave && ~user_has_octave_forge_package('optim')
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error('Optimization algorithm ''fminsearch'' requires the optim package')
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elseif ~isoctave && ~user_has_matlab_license('optimization_toolbox')
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error('Optimization algorithm ''fminsearch'' requires the Optimization Toolbox')
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end
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minalgo = 7;
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case 'simplex'
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minalgo = 8;
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case 'annealing'
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minalgo = 2;
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bounds = ones(length(params0),1)*[-Inf,Inf];
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bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).ec.params)),1) = 0.0;
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bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).ec.params)),2) = 1.0;
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parameter_names = fieldnames(params);
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case 'particleswarm'
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if isoctave
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error('Optimization ''particleswarm'' is not available under Octave')
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elseif ~user_has_matlab_license('global_optimization_toolbox')
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error('Optimization ''particleswarm'' requires the Global Optimization Toolbox')
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end
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minalgo = 12;
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bounds = ones(length(params0),1)*[-Inf,Inf];
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bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).ec.params)),1) = 0.0;
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bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).ec.params)),1) = 1.0;
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parameter_names = fieldnames(params);
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otherwise
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msg = sprintf('%s is not an implemented optimization routine.\n', optimizer);
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msg = sprintf('%sAvailable algorithms are:\n', msg);
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msg = sprintf('%s - %s\n', msg, 'fmincon');
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msg = sprintf('%s - %s\n', msg, 'fminunc');
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msg = sprintf('%s - %s\n', msg, 'csminwel');
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msg = sprintf('%s - %s\n', msg, 'fminsearch');
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msg = sprintf('%s - %s\n', msg, 'simplex');
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msg = sprintf('%s - %s\n', msg, 'annealing');
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msg = sprintf('%s - %s\n', msg, 'lsqnonlin');
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msg = sprintf('%s - %s\n', msg, 'GaussNewton');
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error(msg)
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end
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end
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% Set options if provided as input arguments to nls routine.
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oldopt = options_.optim_opt;
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[noprint, opt] = opt4nls(varargin);
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options_.optim_opt = opt;
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%
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% Check that we are able to evaluate the Sum of Squared Residuals on the initial guess
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%
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ssr0 = ssrfun(params0);
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if isnan(ssr0) || isinf(ssr0) || ~isreal(ssr0)
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error('Cannot evaluate the Sum of Squared Residuals on the initial guess.')
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end
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if is_gauss_newton
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[params1, SSR, exitflag] = gauss_newton(resfun, params0);
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elseif is_lsqnonlin
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if ismember('levenberg-marquardt', varargin)
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% Levenberg Marquardt does not handle boundary constraints.
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[params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, [], [], optimset(varargin{:}));
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else
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[params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, bounds(:,1), bounds(:,2), optimset(varargin{:}));
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end
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else
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% Estimate the parameters by minimizing the sum of squared residuals.
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[params1, SSR, exitflag] = dynare_minimize_objective(ssrfun, params0, ...
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minalgo, ...
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options_, ...
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bounds, ...
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parameter_names, ...
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[], ...
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[]);
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end
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% Revert local modifications to the options.
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options_.optim_opt = oldopt;
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% Compute an estimator of the covariance matrix (see White and
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% Domovitz [Econometrica, 1984], theorem 3.2).
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[r, J] = jacobian(resfun, params1, 1e-6);
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T = length(r);
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A = 2.0*(J'*J)/T;
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J = bsxfun(@times, J, r);
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B = J'*J;
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l = round(T^.25);
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for tau=1:l
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B = B + (1-tau/(l+1))*(J(tau+1:end,:)'*J(1:end-tau,:)+J(1:end-tau,:)'*J(tau+1:end,:));
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end
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B = (4.0/T)*B;
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C = inv(A)*B*inv(A); % C is the asymptotic covariance of sqrt(T) times the vector of estimated parameters.
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C = C/T;
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% Save results
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lhs = eval(strrep(lhs, 'data', 'data(range(1)-1:range(end)).data'));
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oo_.pac.(pacmodl).lhs = dseries(lhs, range(1), 'lhs');
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oo_.pac.(pacmodl).fit = dseries(lhs-r, range(1), 'fit');
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oo_.pac.(pacmodl).residual = dseries(r, range(1), 'residual');
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oo_.pac.(pacmodl).ssr = SSR;
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oo_.pac.(pacmodl).s2 = SSR/T;
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oo_.pac.(pacmodl).R2 = 1-var(r)/var(lhs);
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oo_.pac.(pacmodl).parnames = fieldnames(params);
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oo_.pac.(pacmodl).estimator = params1;
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oo_.pac.(pacmodl).covariance = C;
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oo_.pac.(pacmodl).student = params1./(sqrt(diag(C)));
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% Also save estimated parameters in M_
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M_.params(ipnames_) = params1;
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M_ = pac.update.parameters(pacmodl, M_, oo_, false);
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if ~noprint
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title = ['NLS Estimation of equation ''' eqname ''''];
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preamble = {['Dependent Variable: ' dLHS], ...
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sprintf('Observations: %d from %s to %s\n', (range(end)-range(1))+1, range(1).char, range(end).char)};
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afterward = {sprintf('R^2: %f', oo_.pac.(pacmodl).R2), ...
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sprintf('s^2: %f', oo_.pac.(pacmodl).s2)}; ...
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dyn_table(title, preamble, afterward, oo_.pac.(pacmodl).parnames, ...
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{'Estimates','t-statistic','Std. Error'}, 4, ...
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[oo_.pac.(pacmodl).estimator oo_.pac.(pacmodl).student sqrt(diag(C))]);
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end
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