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76ca912d02
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@ -4609,7 +4609,8 @@ where @var{THEORETICAL_MOMENT} is one of the following:
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Variance-covariance of endogenous variables
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@item correlation
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Autocorrelation of endogenous variables
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Auto- and cross-correlation of endogenous variables. Fields are vectors with correlations from 1 up to order @code{options_.ar}
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@item VarianceDecomposition
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Decomposition of variance@footnote{When the shocks are correlated, it
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@ -49,9 +49,9 @@ if isfield(oo_,[TYPE 'TheoreticalMoments'])
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge;'])
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if isfield(temporary_structure,'correlation')
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eval(['temporary_structure = oo_.' TYPE 'TheoreticalMoments.dsge.correlation.Mean;'])
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if isfield(temporary_structure,var1)
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if isfield(temporary_structure,deblank(var1))
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eval(['temporary_structure_1 = oo_.' TYPE 'TheoreticalMoments.dsge.correlation.Mean.' var1 ';'])
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if isfield(temporary_structure_1,var2)
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if isfield(temporary_structure_1,deblank(var2))
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eval(['temporary_structure_2 = temporary_structure_1.' var2 ';'])
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l1 = length(temporary_structure_2);
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if l1<nar
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@ -116,7 +116,7 @@ else
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if isfield(temporary_structure,'correlation')
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Mean',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Median',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Nariance',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Variance',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'HPDinf',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'HPDsup',nar,NaN);
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oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,NaN);
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