237 lines
8.1 KiB
Matlab
237 lines
8.1 KiB
Matlab
function ds = dyn_ols(ds, fitted_names_dict, eqtags, model_name, param_names, ds_range, update_params)
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% function varargout = dyn_ols(ds, fitted_names_dict, eqtags, model_name, param_names, ds_range)
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% Run OLS on chosen model equations; unlike olseqs, allow for time t
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% endogenous variables on LHS
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%
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% INPUTS
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% ds [dseries] data
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% fitted_names_dict [cell] Nx2 or Nx3 cell array to be used in naming fitted
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% values; first column is the equation tag,
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% second column is the name of the
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% associated fitted value, third column
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% (if it exists) is the function name of
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% the transformation to perform on the
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% fitted value.
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% eqtags [cellstr] names of equation tags to estimate. If empty,
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% estimate all equations
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% model_name [celltsr] name to use in oo_ and inc file (must be
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% same size as eqtags)
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% param_names [cell of cellstr] list of parameters to estimate by eqtag
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% (if empty, estimate all)
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% ds_range [dates] range of dates to use in estimation
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% update_params [logical] If false M_.params will not be estimation.
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% If true (default) M_.params will be updated.
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%
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% OUTPUTS
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% ds [dseries] data updated with fitted values
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%
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% SPECIAL REQUIREMENTS
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% dynare must have been run with the option: json=compute
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% Copyright © 2017-2021 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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if nargin < 1 || nargin > 7
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error('dyn_ols() takes between 1 and 7 arguments')
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end
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if isempty(ds) || ~isdseries(ds)
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error('dyn_ols: the first argument must be a dseries')
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end
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if nargin == 6 && ~isempty(ds_range)
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update_params = true;
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elseif nargin < 6
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ds_range = ds.dates;
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update_params = true;
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else
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if isempty(ds_range)
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ds_range = ds.dates;
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else
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if ds_range(1) < ds.firstdate || ds_range(end) > lastdate(ds)
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error('There is a problem with the 6th argument: the date range does not correspond to that of the dseries')
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end
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end
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update_params = true;
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end
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if nargin < 5
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param_names = {};
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else
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if ~isempty(param_names)
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if ~iscell(param_names) || (~isempty(eqtags) && length(param_names) ~= length(eqtags))
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error('The 5th argument, if provided, must be a cell of the same length as the eqtags argument')
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end
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for i = 1:length(param_names)
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if ~iscellstr(param_names{i})
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error('every entry of param_names must be a cellstr')
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end
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end
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end
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end
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if nargin < 3
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eqtags = {};
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end
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if nargin < 2
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fitted_names_dict = {};
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else
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assert(isempty(fitted_names_dict) || ...
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(iscell(fitted_names_dict) && ...
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(size(fitted_names_dict, 2) == 2 || size(fitted_names_dict, 2) == 3)), ...
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'dyn_ols: the second argument must be an Nx2 or Nx3 cell array');
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end
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%% Get Equation(s)
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ast = get_ast(eqtags);
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%% Set model_name
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if nargin < 4
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model_name = cell(length(ast), 1);
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else
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if isempty(model_name)
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model_name = repmat({''}, length(ast), 1);
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else
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if ~iscellstr(model_name) || length(model_name) ~= length(ast)
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error('The length of the 4th argument must be a cellstr with length equal to the number of equations estimated')
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end
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for i = 1:length(model_name)
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if ~isvarname(model_name{i})
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error('Every entry in the 4th argument must be a valid string');
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end
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end
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end
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end
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%% Parse equations
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[Y, lhssub, X, fp, lp] = common_parsing(ds(ds_range), ast, true, param_names);
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%% Loop over equations
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for i = 1:length(Y)
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pnames = X{i}.name;
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[nobs, nvars] = size(X{i}.data);
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if ~isempty(model_name{i})
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tag = model_name{i};
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else
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if isfield(ast{i}, 'tags') && isfield(ast{i}.tags, 'name')
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tag = ast{i}.tags.('name');
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else
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tag = ['eq_line_no_' num2str(ast{i}.line)];
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end
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end
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%% Estimation
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% From LeSage, James P. "Applied Econometrics using MATLAB"
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oo_.ols.(tag).dof = nobs - nvars;
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% Estimated Parameters
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[q, r] = qr(X{i}.data, 0);
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xpxi = (r'*r)\eye(nvars);
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oo_.ols.(tag).beta = r\(q'*Y{i}.data);
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oo_.ols.(tag).param_idxs = zeros(length(pnames), 1);
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for j = 1:length(pnames)
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if ~strcmp(pnames{j}, 'intercept')
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oo_.ols.(tag).param_idxs(j) = find(strcmp(M_.param_names, pnames{j}));
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if update_params
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M_.params(oo_.ols.(tag).param_idxs(j)) = oo_.ols.(tag).beta(j);
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end
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end
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end
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% Write .inc file
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write_param_init_inc_file('ols', tag, oo_.ols.(tag).param_idxs, oo_.ols.(tag).beta);
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% Yhat
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idx = 0;
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yhatname = [tag '_FIT'];
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if ~isempty(fitted_names_dict)
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idx = strcmp(fitted_names_dict(:,1), tag);
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if any(idx)
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yhatname = fitted_names_dict{idx, 2};
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end
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end
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oo_.ols.(tag).Yhat = dseries(X{i}.data*oo_.ols.(tag).beta, fp{i}, yhatname);
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% Residuals
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oo_.ols.(tag).resid = Y{i} - oo_.ols.(tag).Yhat;
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% Correct Yhat reported back to user
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Y{i} = Y{i} + lhssub{i};
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oo_.ols.(tag).Yobs = Y{i};
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oo_.ols.(tag).Yhat = oo_.ols.(tag).Yhat + lhssub{i};
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oo_.ols.(tag).YhatOrig = oo_.ols.(tag).Yhat;
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% Apply correcting function for Yhat if it was passed
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if any(idx) ...
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&& length(fitted_names_dict(idx, :)) == 3 ...
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&& ~isempty(fitted_names_dict{idx, 3})
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oo_.ols.(tag).Yhat = ...
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feval(fitted_names_dict{idx, 3}, oo_.ols.(tag).Yhat);
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end
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ds.(oo_.ols.(tag).Yhat.name{:}) = oo_.ols.(tag).Yhat;
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%% Calculate statistics
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% Estimate for sigma^2
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SS_res = oo_.ols.(tag).resid.data'*oo_.ols.(tag).resid.data;
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oo_.ols.(tag).s2 = SS_res/oo_.ols.(tag).dof;
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% R^2
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ym = Y{i}.data - mean(Y{i});
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SS_tot = ym'*ym;
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oo_.ols.(tag).R2 = 1 - SS_res/SS_tot;
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% Adjusted R^2
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oo_.ols.(tag).adjR2 = oo_.ols.(tag).R2 - (1 - oo_.ols.(tag).R2)*(nvars-1)/(oo_.ols.(tag).dof);
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% Durbin-Watson
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ediff = oo_.ols.(tag).resid.data(2:nobs) - oo_.ols.(tag).resid.data(1:nobs-1);
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oo_.ols.(tag).dw = (ediff'*ediff)/SS_res;
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% Standard Error
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oo_.ols.(tag).stderr = sqrt(oo_.ols.(tag).s2*diag(xpxi));
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% T-Stat
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oo_.ols.(tag).tstat = oo_.ols.(tag).beta./oo_.ols.(tag).stderr;
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%% Print Output
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if ~options_.noprint
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if nargin == 3
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title = ['OLS Estimation of equation ''' tag ''' [name = ''' tag ''']'];
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else
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title = ['OLS Estimation of equation ''' tag ''''];
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end
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preamble = {['Dependent Variable: ' Y{i}.name{:}], ...
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sprintf('No. Independent Variables: %d', nvars), ...
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sprintf('Observations: %d from %s to %s\n', nobs, fp{i}.char, lp{i}.char)};
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afterward = {sprintf('R^2: %f', oo_.ols.(tag).R2), ...
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sprintf('R^2 Adjusted: %f', oo_.ols.(tag).adjR2), ...
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sprintf('s^2: %f', oo_.ols.(tag).s2), ...
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sprintf('Durbin-Watson: %f', oo_.ols.(tag).dw)};
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dyn_table(title, preamble, afterward, pnames, ...
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{'Estimates','t-statistic','Std. Error'}, 4, ...
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[oo_.ols.(tag).beta oo_.ols.(tag).tstat oo_.ols.(tag).stderr]);
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end
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end
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end
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