61 lines
1.7 KiB
Modula-2
61 lines
1.7 KiB
Modula-2
// --+ options: json=compute +--
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// Declare the endogenous variables.
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var ffr, unrate, cpi;
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// Declare the exogenous variables.
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varexo e_ffr, e_unrate, e_cpi;
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// Declare the model.
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model(linear);
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[name='ffr']
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ffr = adl(ffr, 'p_ffr_ffr', [1:3]) + adl(unrate, 'p_ffr_unrate', 1) + adl(cpi, 'p_ffr_cpi', [4]) + e_ffr;
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[name='unrate']
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unrate = adl(unrate, 'p_ffr_unrate', [4 2 5]) + adl(cpi, 'p_unrate_cpi', 6) + e_unrate;
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[name='cpi']
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cpi = adl(ffr, 'p_cpi_ffr', 2) + adl(cpi, 'p_cpi_cpi', [2]) + e_cpi;
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end;
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// Set true values for the parameters (randomly, I write directly in M_.params
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// because I am too lazy to write all the parameter names).
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M_.params = .1*randn(M_.param_nbr, 1);
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shocks;
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var e_ffr; stderr .01;
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var e_unrate; stderr .01;
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var e_cpi; stderr .01;
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end;
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// Create a dataset by simulating the model.
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oo_ = simul_backward_model([], 1000, options_, M_, oo_);
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// Put all the simulated data in a dseries object.
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ds1 = dseries(transpose(oo_.endo_simul), 1900Q1, M_.endo_names);
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// Select a subsample for estimation
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ds1 = ds1(1951Q1:1990Q1);
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// Print true values of the parameters (for comparison with the estimation results).
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dyn_table('True values for the parameters (DGP)', {}, {}, cellstr(M_.param_names), ...
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{'Parameter Value'}, 4, M_.params);
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/* Run the estimation of equation ffr and display the results. Note that the corresponding
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** values in M_.params will be updated.
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**
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** estimate command
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** ----------------
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**
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** First argument is used to specify the estimation method
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** Second argument is used to specify the dataset (an existing dseries object).
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** Third argument (and following) is(are) the name(s) of equation(s) to be estimated.
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*/
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estimate method(ols) data(ds1) ffr;
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