50 lines
1.2 KiB
Modula-2
50 lines
1.2 KiB
Modula-2
// --+ options: json=compute +--
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% Example from Section 6.1 of
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% Ando, Tomohiro and Zellner, Arnold. 2010. Hierarchical Bayesian Analysis of the
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% Seemingly Unrelated Regression and Simultaneous Equations Models Using a
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% Combination of Direct Monte Carlo and Importance Sampling Techniques.
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% Bayesian Analysis Volume 5, Number 1, pp. 65-96.
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var qty, price;
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varexo res_u, res_v, stormy, mixed;
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parameters bq0, bp0, bq1, bp1, bp2;
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bp0 = 8.5527;
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bp1 = -0.5302;
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bp2 = -0.3974;
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bq0 = 6.7523;
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bq1 = -0.7969;
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model(linear);
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qty = bq0 + bq1*price + res_u;
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price = bp0 + bp1*stormy + bp2*mixed + res_v;
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end;
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% Estimate all parameters
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%estparams = M_.param_names;
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%estparamsval = M_.params;
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% Estimate demand parameters
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estparams = {'bq1' 'bq0'};
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estparamsval = [bq1 bq0];
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A = 0.0005.*eye(length(estparams));
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simdata = surgibbs(dseries('fishdata.csv'), estparams, estparamsval, A, 20000, 5000, 7);
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good = [6.791587808530124
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8.552700000000000
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-0.478275288902356
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-0.530200000000000
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-0.397400000000000];
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if isoctave
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tolerance = 1e-2;
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else
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tolerance = 2e-14;
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end
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if sum(abs(M_.params-good)) > tolerance
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error(['sum of M_.params - good was: ' num2str(sum(abs(M_.params-good)))]);
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end
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