removing 8th output argument of dynare_estimation_init and
corresponding seemingly useless codetime-shift
parent
88be4fa3d4
commit
bd00dc11d8
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@ -37,7 +37,7 @@ else
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objective_function = str2func('DsgeVarLikelihood');
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end
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[dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_, fake] = dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
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[dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
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data = dataset_.data;
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rawdata = dataset_.rawdata;
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@ -308,81 +308,3 @@ end
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dataset_ = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);
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options_.nobs = dataset_.info.ntobs;
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% $$$ rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
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% $$$ % Set the number of observations (nobs) and build a subsample between first_obs and nobs.
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% $$$ options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
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% $$$ gend = options_.nobs;
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% $$$ rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
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% $$$ % Take the log of the variables if needed
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% $$$ if options_.loglinear % If the model is log-linearized...
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% $$$ if ~options_.logdata % and if the data are not in logs, then...
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% $$$ rawdata = log(rawdata);
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% $$$ end
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% $$$ end
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% $$$ % Test if the observations are real numbers.
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% $$$ if ~isreal(rawdata)
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% $$$ error('There are complex values in the data! Probably a wrong transformation')
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% $$$ end
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% $$$ % Test for missing observations.
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% $$$ options_.missing_data = any(any(isnan(rawdata)));
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% $$$ % Prefilter the data if needed.
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% $$$ if options_.prefilter == 1
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% $$$ if options_.missing_data
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% $$$ bayestopt_.mean_varobs = zeros(n_varobs,1);
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% $$$ for variable=1:n_varobs
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% $$$ rdx = find(~isnan(rawdata(:,variable)));
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% $$$ m = mean(rawdata(rdx,variable));
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% $$$ rawdata(rdx,variable) = rawdata(rdx,variable)-m;
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% $$$ bayestopt_.mean_varobs(variable) = m;
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% $$$ end
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% $$$ else
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% $$$ bayestopt_.mean_varobs = mean(rawdata,1)';
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% $$$ rawdata = rawdata-repmat(bayestopt_.mean_varobs',gend,1);
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% $$$ end
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% $$$ end
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% $$$ % Transpose the dataset array.
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% $$$ data = transpose(rawdata);
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if nargout>7
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% Compute the steady state:
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if options_.steadystate_flag% if the *_steadystate.m file is provided.
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[ys,tchek] = feval([M_.fname '_steadystate'],...
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[zeros(M_.exo_nbr,1);...
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oo_.exo_det_steady_state]);
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if size(ys,1) < M_.endo_nbr
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if length(M_.aux_vars) > 0
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ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,...
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M_.fname,...
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zeros(M_.exo_nbr,1),...
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oo_.exo_det_steady_state,...
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M_.params,...
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options_.bytecode);
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else
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error([M_.fname '_steadystate.m doesn''t match the model']);
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end
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end
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oo_.steady_state = ys;
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else% if the steady state file is not provided.
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[dd,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
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oo_.steady_state = dd.ys; clear('dd');
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end
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if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
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options_.noconstant = 1;
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else
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options_.noconstant = 0;
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end
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fake = [];
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% $$$ [data_index,number_of_observations,no_more_missing_observations] = describe_missing_data(data,gend,n_varobs);
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% $$$ missing_value = ~(number_of_observations == gend*n_varobs);
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% $$$
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% $$$ % initial_estimation_checks(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
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% $$$
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% $$$ data_info.gend = gend;
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% $$$ data_info.data = data;
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% $$$ data_info.data_index = data_index;
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% $$$ data_info.number_of_observations = number_of_observations;
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% $$$ data_info.no_more_missing_observations = no_more_missing_observations;
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% $$$ data_info.missing_value = missing_value;
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end
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