function nls(eqname, params, data, range, optimizer, varargin)

% Estimates the parameters of a PAC equation by Nonlinear Least Squares.
%
% INPUTS
% - eqname       [string]    Name of the pac equation.
% - params       [struct]    Describes the parameters to be estimated.
% - data         [dseries]   Database for the estimation
% - range        [dates]     Range of dates for the estimation.
% - optimizer    [string]    Set the optimization algorithm. Possible values
%                            are 'fmincon', 'fminunc', 'csminwel',
%                            'fminsearch', 'simplex' and
%                            'annealing'. Default is 'csminwel'.
% - varargin                 List of key/value pairs, each key must be a
%                            string and values can be strings or real numbers.
%
% OUTPUTS
% - none
%
% REMARKS
% [1] The estimation results are printed in the command line window, and the
%     parameters are updated accordingly in M_.params.
% [2] The second input is a structure. Each fieldname corresponds to the
%     name of an estimated parameter, the value of the field is the initial
%     guess used for the estimation (by NLS).
% [3] The third input is a dseries object which must at least contain all
%     the variables appearing in the estimated equation. The residual of the
%     equation must have NaN values in the object.
% [4] It is assumed that the residual is additive.
% [5] If the used optimization routines handles inequalities over the
%     estimated parameters, we impose the positivity of the error correction parameter.
%
% EXAMPLE
%
% pac.estimate.nls('zpac', eparams, dbase, dates('2003Q1'):dates('2016Q3'),'fminunc', 'MaxIter', 50);
%
% where zpac is the name of the PAC equation, eparams is a structure
% containing the guess values for the estimated parameters (in each field),
% dbase is a dseries object containing the data,
% dates('2003Q1'):dates('2016Q3') is the range of the sample used for
% estimation, 'fminunc' is the name of the optimization algorithm (this one
% is available only if the matylab optimization toolbox is installed), the
% remaining inputs are the options (key/value) passed to the optimizers.

% Copyright (C) 2018-2021 Dynare Team
%
% This file is part of Dynare.
%
% Dynare 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.
%
% Dynare 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 Dynare.  If not, see <http://www.gnu.org/licenses/>.

global M_ oo_ options_

is_gauss_newton = false;
is_lsqnonlin = false;
if nargin>4 && (isequal(optimizer, 'GaussNewton') || isequal(optimizer, 'lsqnonlin'))
    switch optimizer
      case 'GaussNewton'
        is_gauss_newton = true;
      case 'lsqnonlin'
        is_lsqnonlin = true;
    end
end

[pacmodl, lhs, rhs, pnames, enames, xnames, ~, pid, eid, xid, ~, ipnames_, params, data, islaggedvariables, eqtag] = ...
    pac.estimate.init(M_, oo_, eqname, params, data, range);

% Check that the error correction term is correct.
if M_.pac.(pacmodl).equations.(eqtag).ec.istarget(2)
    error(['\nThe error correction term in PAC equation (%s) is not correct.\nThe ' ...
           'error correction term should be the difference between a trend\n' ...
           'and the level of the endogenous variable.'], pacmodl);
end

% List of objects to be replaced
objNames = [pnames; enames; xnames];
objIndex = [pid; eid; xid];
objTypes = [ones(length(pid), 1); 2*ones(length(eid), 1); 3*ones(length(eid), 1);];

[~,I] = sort(cellfun(@length, objNames), 'descend');
objNames = objNames(I);
objIndex = objIndex(I);
objTypes = objTypes(I);

% Substitute parameters and variables.
for i=1:length(objNames)
    switch objTypes(i)
      case 1
        rhs = strrep(rhs, objNames{i}, sprintf('DynareModel.params(%u)', objIndex(i)));
      case {2,3}
        k = find(strcmp(objNames{i}, data.name));
        if isempty(k)
            error('Variable %s is missing in the database.', objNames{i})
        end
        j = regexp(rhs, ['\<', objNames{i}, '\>']);
        if islaggedvariables
            jlag = regexp(rhs, ['\<', objNames{i}, '\(-1\)']);
            if ~isempty(jlag)
                rhs = regexprep(rhs, ['\<' objNames{i} '\(-1\)'], sprintf('data(1:end-1,%u)', k));
            end
            if ~isempty(setdiff(j, jlag))
                rhs = regexprep(rhs, ['\<' objNames{i} '\>'], sprintf('data(2:end,%u)', k));
            end
        else
            rhs = regexprep(rhs, ['\<' objNames{i} '\>'], sprintf('data(:,%u)', k));
        end
        if contains(lhs, objNames{i})
            if islaggedvariables
                lhs = strrep(lhs, objNames{i}, sprintf('data(2:end,%u)', k));
            else
                lhs = strrep(lhs, objNames{i}, sprintf('data(:,%u)', k));
            end
        end
    end
end

% Create a routine for evaluating the residuals of the nonlinear model
fun = ['r_' eqname];
fid = fopen(['+' M_.fname filesep() fun '.m'], 'w');
fprintf(fid, 'function r = %s(params, data, DynareModel, DynareOutput)\n', fun);
fprintf(fid, '\n');
fprintf(fid, '%% Evaluates the residuals for equation %s.\n', eqname);
fprintf(fid, '%% File created by Dynare (%s).\n', datetime);
fprintf(fid, '\n');
for i=1:length(ipnames_)
    fprintf(fid, 'DynareModel.params(%u) = params(%u);\n', ipnames_(i), i);
end
fprintf(fid, '\n');
fprintf(fid, 'DynareModel = pac.update.parameters(''%s'', DynareModel, DynareOutput, false);\n', pacmodl);
fprintf(fid, '\n');
fprintf(fid, 'r = %s-(%s);\n', lhs, rhs);
fclose(fid);

% Create a routine for evaluating the sum of squared residuals of the nonlinear model
fun = ['ssr_' eqname];
fid = fopen(['+' M_.fname filesep() fun '.m'], 'w');
fprintf(fid, 'function [s, fake1, fake2, fake3, fake4] = %s(params, data, DynareModel, DynareOutput)\n', fun);
fprintf(fid, '\n');
fprintf(fid, '%% Evaluates the sum of square residuals for equation %s.\n', eqname);
fprintf(fid, '%% File created by Dynare (%s).\n', datetime);
fprintf(fid, '\n');
fprintf(fid, 'fake1 = 0;\n');
fprintf(fid, 'fake2 = [];\n');
fprintf(fid, 'fake3 = [];\n');
fprintf(fid, 'fake4 = [];\n');
fprintf(fid, '\n');
for i=1:length(ipnames_)
    fprintf(fid, 'DynareModel.params(%u) = params(%u);\n', ipnames_(i), i);
end
fprintf(fid, '\n');
fprintf(fid, 'DynareModel = pac.update.parameters(''%s'', DynareModel, DynareOutput, false);\n', pacmodl);
fprintf(fid, '\n');
fprintf(fid, 'r = %s-(%s);\n', lhs, rhs);
fprintf(fid, 's = r''*r;\n');
fclose(fid);

% Copy (sub)sample data in a matrix.
DATA = data([range(1)-1, range]).data;

% Create a function handle returning the sum of square residuals for a given vector of parameters.
ssrfun = @(p) feval([M_.fname '.ssr_' eqname], p, DATA, M_, oo_);

% Create a function handle returning the sum of square residuals for a given vector of parameters.
resfun = @(p) feval([M_.fname '.r_' eqname], p, DATA, M_, oo_);

% Set initial condition.
params0 = cell2mat(struct2cell(params));

% Set defaults for optimizers
bounds = [];
parameter_names = [];

% Set optimizer routine.
if nargin<5 || isempty(optimizer)
    % Use csminwel by default.
    minalgo = 4;
else
    switch optimizer
      case 'GaussNewton'
        % Nothing to do here.
      case 'lsqnonlin'
        bounds = ones(length(params0),1)*[-Inf,Inf];
        bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).equations.(eqtag).ec.params)),1)  = 0.0;
        bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).equations.(eqtag).ec.params)),2)  = 1.0;
      case 'fmincon'
        if isoctave && ~user_has_octave_forge_package('optim', '1.6')
            error('Optimization algorithm ''fmincon'' requires the optim package, version 1.6 or higher')
        elseif ~isoctave && ~user_has_matlab_license('optimization_toolbox')
            error('Optimization algorithm ''fmincon'' requires the Optimization Toolbox')
        end
        minalgo = 1;
        bounds = ones(length(params0),1)*[-Inf,Inf];
        bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).equations.(eqtag).ec.params)),1)  = 0.0;
        bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).equations.(eqtag).ec.params)),2)  = 1.0;
      case 'fminunc'
        if isoctave && ~user_has_octave_forge_package('optim')
            error('Optimization algorithm ''fminunc'' requires the optim package')
        elseif ~isoctave && ~user_has_matlab_license('optimization_toolbox')
            error('Optimization algorithm ''fminunc'' requires the Optimization Toolbox')
        end
        minalgo = 3;
      case 'csminwel'
        minalgo = 4;
      case 'fminsearch'
        if isoctave && ~user_has_octave_forge_package('optim')
            error('Optimization algorithm ''fminsearch'' requires the optim package')
        elseif ~isoctave && ~user_has_matlab_license('optimization_toolbox')
            error('Optimization algorithm ''fminsearch'' requires the Optimization Toolbox')
        end
        minalgo = 7;
      case 'simplex'
        minalgo = 8;
      case 'annealing'
        minalgo = 2;
        bounds = ones(length(params0),1)*[-Inf,Inf];
        bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).equations.(eqtag).ec.params)),1)  = 0.0;
        bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).equations.(eqtag).ec.params)),2)  = 1.0;
        parameter_names = fieldnames(params);
      case 'particleswarm'
        if isoctave
            error('Optimization ''particleswarm'' is not available under Octave')
        elseif ~user_has_matlab_license('global_optimization_toolbox')
            error('Optimization ''particleswarm'' requires the Global Optimization Toolbox')
        end
        minalgo = 12;
        bounds = ones(length(params0),1)*[-Inf,Inf];
        bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).equations.(eqtag).ec.params)),1)  = 0.0;
        bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.(pacmodl).equations.(eqtag).ec.params)),1)  = 1.0;
        parameter_names = fieldnames(params);
      otherwise
        msg = sprintf('%s is not an implemented optimization routine.\n', optimizer);
        msg = sprintf('%sAvailable algorithms are:\n', msg);
        msg = sprintf('%s - %s\n', msg, 'fmincon');
        msg = sprintf('%s - %s\n', msg, 'fminunc');
        msg = sprintf('%s - %s\n', msg, 'csminwel');
        msg = sprintf('%s - %s\n', msg, 'fminsearch');
        msg = sprintf('%s - %s\n', msg, 'simplex');
        msg = sprintf('%s - %s\n', msg, 'annealing');
        msg = sprintf('%s - %s\n', msg, 'lsqnonlin');
        msg = sprintf('%s - %s\n', msg, 'GaussNewton');
        error(msg)
    end
end

% Set options if provided as input arguments to nls routine.
oldopt = options_.optim_opt;
if nargin>5
    if mod(nargin-5, 2)
        error('Options must come by key/value pairs.')
    end
    i = 1;
    opt = '';
    while i<nargin-5
        if i==1
            opt = sprintf('''%s''', varargin{i});
        else
            opt = sprintf('%s,''%s''', opt, varargin{i});
        end
        if isnumeric(varargin{i+1})
            opt = sprintf('%s,%s', opt, num2str(varargin{i+1}));
        else
            opt = sprintf('%s,''%s''', opt, varargin{i+1});
        end
        i = i+2;
    end
    options_.optim_opt = opt;
else
    options_.optim_opt = [];
end
if nargin<5
    % If default optimization algorithm is used (csminwel), do not print
    % iterations.
    options_.optim_opt = '''verbosity'',0';
end

if is_gauss_newton
    [params1, SSR, exitflag] = gauss_newton(resfun, params0);
elseif is_lsqnonlin
    if ismember('levenberg-marquardt', varargin)
        % Levenberg Marquardt does not handle boundary constraints.
        [params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, [], [], optimset(varargin{:}));
    else
        [params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, bounds(:,1), bounds(:,2), optimset(varargin{:}));
    end
else
    % Estimate the parameters by minimizing the sum of squared residuals.
    [params1, SSR, exitflag] = dynare_minimize_objective(ssrfun, params0, ...
                                                      minalgo, ...
                                                      options_, ...
                                                      bounds, ...
                                                      parameter_names, ...
                                                      [], ...
                                                      []);
end

% Revert local modifications to the options.
options_.optim_opt = oldopt;

% Compute an estimator of the covariance matrix (see White and
% Domovitz [Econometrica, 1984], theorem 3.2).
[r, J] = jacobian(resfun, params1, 1e-6);
T = length(r);
A = 2.0*(J'*J)/T;
J = bsxfun(@times, J, r);
B = J'*J;
l = round(T^.25);
for tau=1:l
    B = B + (1-tau/(l+1))*(J(tau+1:end,:)'*J(1:end-tau,:)+J(1:end-tau,:)'*J(tau+1:end,:));
end
B = (4.0/T)*B;
C = inv(A)*B*inv(A); % C is the asymptotic covariance of sqrt(T) times the vector of estimated parameters.
C = C/T;

% Save results
lhs = eval(strrep(lhs, 'data', 'data(range(1)-1:range(end)).data'));
oo_.pac.(pacmodl).equations.(eqtag).lhs = dseries(lhs, range(1), sprintf('%s_lhs', eqtag));
oo_.pac.(pacmodl).equations.(eqtag).fit = dseries(lhs-r, range(1), sprintf('%s_fit', eqtag));
oo_.pac.(pacmodl).equations.(eqtag).residual = dseries(r, range(1), sprintf('%s_residual', eqtag));
oo_.pac.(pacmodl).equations.(eqtag).ssr = SSR;
oo_.pac.(pacmodl).equations.(eqtag).R2 = 1-var(r)/var(lhs);
oo_.pac.(pacmodl).equations.(eqtag).parnames = fieldnames(params);
oo_.pac.(pacmodl).equations.(eqtag).estimator = params1;
oo_.pac.(pacmodl).equations.(eqtag).covariance = C;
oo_.pac.(pacmodl).equations.(eqtag).student = params1./(sqrt(diag(C)));

% Also save estimated parameters in M_
M_.params(ipnames_) = params1;
M_ = pac.update.parameters(pacmodl, M_, oo_, false);