initial_estimation_checks.m 11 KB
Newer Older
1
function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,DatasetInfo,Model,EstimatedParameters,DynareOptions,BayesInfo,BoundsInfo,DynareResults)
2
% function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,DatasetInfo,Model,EstimatedParameters,DynareOptions,BayesInfo,BoundsInfo,DynareResults)
assia's avatar
assia committed
3
% Checks data (complex values, ML evaluation, initial values, BK conditions,..)
4
%
assia's avatar
assia committed
5
% INPUTS
6
7
%   objective_function  [function handle] of the objective function
%   xparam1:            [vector] of parameters to be estimated
8
9
%   DynareDataset:      [dseries] object storing the dataset
%   DataSetInfo:        [structure] storing informations about the sample.
10
11
12
13
%   Model:              [structure] decribing the model
%   EstimatedParameters [structure] characterizing parameters to be estimated
%   DynareOptions       [structure] describing the options
%   BayesInfo           [structure] describing the priors
14
%   BoundsInfo          [structure] containing prior bounds
15
%   DynareResults       [structure] storing the results
16
%
assia's avatar
assia committed
17
% OUTPUTS
18
%    DynareResults     structure of temporary results
19
%
assia's avatar
assia committed
20
21
22
% SPECIAL REQUIREMENTS
%    none

23
% Copyright (C) 2003-2018 Dynare Team
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
%
% 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/>.
assia's avatar
assia committed
39

40
41
42
43
%get maximum number of simultaneously observed variables for stochastic
%singularity check
maximum_number_non_missing_observations=max(sum(~isnan(DynareDataset.data),2));

Stéphane Adjemian's avatar
Stéphane Adjemian committed
44
if DynareOptions.order>1
45
46
47
48
49
50
51
52
53
    if any(any(isnan(DynareDataset.data)))
        error('initial_estimation_checks:: particle filtering does not support missing observations')
    end
    if DynareOptions.prefilter==1
        error('initial_estimation_checks:: particle filtering does not support the prefilter option')
    end
    if BayesInfo.with_trend
        error('initial_estimation_checks:: particle filtering does not support trends')
    end
54
55
56
    if Model.H==0
        error('initial_estimation_checks:: particle filtering requires measurement error on the observables')
    else
Stéphane Adjemian's avatar
Stéphane Adjemian committed
57
58
59
60
61
62
63
        if sum(diag(Model.H)>0)<length(DynareOptions.varobs)
            error('initial_estimation_checks:: particle filtering requires as many measurement errors as observed variables')
        else
            [~,flag]=chol(Model.H);
            if flag
                error('initial_estimation_checks:: the measurement error matrix must be positive definite')
            end
64
65
        end
    end
66
67
end

68
69
70
non_zero_ME=length(EstimatedParameters.H_entries_to_check_for_positive_definiteness);

if maximum_number_non_missing_observations>Model.exo_nbr+non_zero_ME
71
72
    error(['initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables!'])
end
73

74
if maximum_number_non_missing_observations>length(find(diag(Model.Sigma_e)))+non_zero_ME
75
    error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance!'])
76
77
end

78
if (any(BayesInfo.pshape  >0 ) && DynareOptions.mh_replic) && DynareOptions.mh_nblck<1
79
    error(['initial_estimation_checks:: Bayesian estimation cannot be conducted with mh_nblocks=0.'])
80
81
end

82
83
84
85
86
old_steady_params=Model.params; %save initial parameters for check if steady state changes param values

% % check if steady state solves static model (except if diffuse_filter == 1)
[DynareResults.steady_state, new_steady_params] = evaluate_steady_state(DynareResults.steady_state,Model,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0);

87
if isfield(EstimatedParameters,'param_vals') && ~isempty(EstimatedParameters.param_vals)
88
89
90
    %check whether steady state file changes estimated parameters
    Model_par_varied=Model; %store Model structure
    Model_par_varied.params(EstimatedParameters.param_vals(:,1))=Model_par_varied.params(EstimatedParameters.param_vals(:,1))*1.01; %vary parameters
91
    [~, new_steady_params_2] = evaluate_steady_state(DynareResults.steady_state,Model_par_varied,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0);
92

93
    changed_par_indices=find((old_steady_params(EstimatedParameters.param_vals(:,1))-new_steady_params(EstimatedParameters.param_vals(:,1))) ...
94
                             | (Model_par_varied.params(EstimatedParameters.param_vals(:,1))-new_steady_params_2(EstimatedParameters.param_vals(:,1))));
95

96
97
    if ~isempty(changed_par_indices)
        fprintf('\nThe steady state file internally changed the values of the following estimated parameters:\n')
98
        disp(char(Model.param_names(EstimatedParameters.param_vals(changed_par_indices,1))))
99
        fprintf('This will override the parameter values drawn from the proposal density and may lead to wrong results.\n')
100
        fprintf('Check whether this is really intended.\n')
101
102
        warning('The steady state file internally changes the values of the estimated parameters.')
    end
103
end
104

105
106
if any(BayesInfo.pshape) % if Bayesian estimation
    nvx=EstimatedParameters.nvx;
107
    if nvx && any(BayesInfo.p3(1:nvx)<0)
108
109
110
111
        warning('Your prior allows for negative standard deviations for structural shocks. Due to working with variances, Dynare will be able to continue, but it is recommended to change your prior.')
    end
    offset=nvx;
    nvn=EstimatedParameters.nvn;
112
    if nvn && any(BayesInfo.p3(1+offset:offset+nvn)<0)
113
114
115
        warning('Your prior allows for negative standard deviations for measurement error. Due to working with variances, Dynare will be able to continue, but it is recommended to change your prior.')
    end
    offset = nvx+nvn;
116
117
    ncx=EstimatedParameters.ncx;
    if ncx && (any(BayesInfo.p3(1+offset:offset+ncx)<-1) || any(BayesInfo.p4(1+offset:offset+ncx)>1))
118
        warning('Your prior allows for correlations between structural shocks larger than +-1 and will not integrate to 1 due to truncation. Please change your prior')
119
120
    end
    offset = nvx+nvn+ncx;
121
122
    ncn=EstimatedParameters.ncn;
    if ncn && (any(BayesInfo.p3(1+offset:offset+ncn)<-1) || any(BayesInfo.p4(1+offset:offset+ncn)>1))
123
        warning('Your prior allows for correlations between measurement errors larger than +-1 and will not integrate to 1 due to truncation. Please change your prior')
124
125
126
    end
end

127
% display warning if some parameters are still NaN
128
test_for_deep_parameters_calibration(Model);
129

130
[lnprior,~,~,info]= priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
131
if any(info)
132
133
134
    fprintf('The prior density evaluated at the initial values is Inf for the following parameters: %s\n',BayesInfo.name{info,1})
    error('The initial value of the prior is -Inf')
end
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149

if DynareOptions.ramsey_policy
    %test whether specification matches
    inst_nbr = size(DynareOptions.instruments,1);
    if inst_nbr~=0
        orig_endo_aux_nbr = Model.orig_endo_nbr + min(find([Model.aux_vars.type] == 6)) - 1;
        implied_inst_nbr = orig_endo_aux_nbr - Model.orig_eq_nbr;
        if inst_nbr>implied_inst_nbr
            error('You have specified more instruments than there are omitted equations')
        elseif inst_nbr<implied_inst_nbr
            error('You have specified fewer instruments than there are omitted equations')
        end
    end
end

150
% Evaluate the likelihood.
151
152
ana_deriv = DynareOptions.analytic_derivation;
DynareOptions.analytic_derivation=0;
153
154
if ~isequal(DynareOptions.mode_compute,11) || ...
        (isequal(DynareOptions.mode_compute,11) && isequal(DynareOptions.order,1))
155
156
157
158
159
160
    %shut off potentially automatic switch to diffuse filter for the
    %purpose of checking stochastic singularity
    use_univariate_filters_if_singularity_is_detected_old=DynareOptions.use_univariate_filters_if_singularity_is_detected;
    DynareOptions.use_univariate_filters_if_singularity_is_detected=0;
    [fval,info] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
    if info(1)==50
161
162
163
164
165
        fprintf('\ninitial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.\n')
        fprintf('initial_estimation_checks:: This is often a sign of stochastic singularity, but can also sometimes happen by chance\n')
        fprintf('initial_estimation_checks:: for a particular combination of parameters and data realizations.\n')
        fprintf('initial_estimation_checks:: If you think the latter is the case, you should try with different initial values for the estimated parameters.\n')
        error('initial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.')
166
    end
167
168
169
    if info(1)==201
        fprintf('initial_estimation_checks:: Initial covariance of the states is not positive definite. Try a different nonlinear_filter_initialization.\n')
        error('initial_estimation_checks:: Initial covariance of the states is not positive definite. Try a different nonlinear_filter_initialization.')
Stéphane Adjemian's avatar
Stéphane Adjemian committed
170
    end
171
    %reset options
172
    DynareOptions.use_univariate_filters_if_singularity_is_detected=use_univariate_filters_if_singularity_is_detected_old;
173
else
174
    info=0;
175
176
    fval = 0;
end
177
178
179
if DynareOptions.debug
    DynareResults.likelihood_at_initial_parameters=fval;
end
180
DynareOptions.analytic_derivation=ana_deriv;
181

182
183
184
185
186
% if DynareOptions.mode_compute==5
%     if ~strcmp(func2str(objective_function),'dsge_likelihood')
%         error('Options mode_compute=5 is not compatible with non linear filters or Dsge-VAR models!')
%     end
% end
187
188
189
190
191
if isnan(fval)
    error('The initial value of the likelihood is NaN')
elseif imag(fval)
    error('The initial value of the likelihood is complex')
end
michel's avatar
michel committed
192

193
if info(1) > 0
194
195
    if DynareOptions.order>1
        [eigenvalues_] = check(Model,DynareOptions, DynareResults);
196
        if any(abs(1-abs(eigenvalues_))<abs(DynareOptions.qz_criterium-1))
197
            error('Your model has at least one unit root and you are using a nonlinear filter. Please set nonlinear_filter_initialization=3.')
198
199
        end
    else
200
201
        disp('Error in computing likelihood for initial parameter values')
        print_info(info, DynareOptions.noprint, DynareOptions)
202
    end
203
204
end

205
206
207
208
209
210
if DynareOptions.prefilter==1
    if (~DynareOptions.loglinear && any(abs(DynareResults.steady_state(BayesInfo.mfys))>1e-9)) || (DynareOptions.loglinear && any(abs(log(DynareResults.steady_state(BayesInfo.mfys)))>1e-9))
        disp(['You are trying to estimate a model with a non zero steady state for the observed endogenous'])
        disp(['variables using demeaned data!'])
        error('You should change something in your mod file...')
    end
211
212
end

213
214
215
if ~isequal(DynareOptions.mode_compute,11)
    disp(['Initial value of the log posterior (or likelihood): ' num2str(-fval)]);
end