40 lines
1.3 KiB
Modula-2
40 lines
1.3 KiB
Modula-2
var y, c, k, a, h, b;
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varexo e, u;
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parameters beta, rho, alpha, delta, theta, psi, tau;
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alpha = 0.36;
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rho = 0.95;
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tau = 0.025;
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beta = 0.99;
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delta = 0.025;
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psi = 0;
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theta = 2.95;
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phi = 0.1;
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model(use_dll);
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c*theta*h^(1+psi)=(1-alpha)*y;
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k = beta*(((exp(b)*c)/(exp(b(+1))*c(+1)))
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*(exp(b(+3))*alpha*y(+2)+(1-delta)*k));
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y = exp(a)*(k(-1)^alpha)*(h^(1-alpha));
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k = exp(b)*(y-c)+(1-delta)*k(-1);
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a = rho*a(-1)+tau*b(-1) + e;
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b = tau*a(-1)+rho*b(-1) + u;
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end;
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markov_switching(chain=1, number_of_regimes=2, duration=2.5, parameters=[alpha, delta, theta], number_of_lags=1);
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alpha.prior (shape=gamma, mean=3.22, variance=0.1^2);
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rho.prior (shape=uniform, mean=322, variance=2^.33, domain=[0.36, 0.88]);
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std(e).prior (shape=beta, mean=0.3, variance=0.1^2, domain=[-0.1 006]);
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std(y).prior (shape=beta, mean=0.3, variance=0.1^2, domain=[01 4]);
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std(c).prior (shape=beta, mean=0.3, variance=0.1^2);
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corr(y,c).prior(shape=beta, mean=0.3, variance=0.1^2, mode=33);
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corr(b,c).prior(shape=beta, mean=0.3, variance=0.1^2);
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corr(e,u).prior(shape=beta, mean=0.3, variance=0.1^2);
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alpha.options(init=1);
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rho.options(init=1);
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beta.options(init=0.2);
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std(u).options(init=3);
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corr(y,c).options(init=.02);
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[alpha , beta , rho].prior(shape=beta, mean=[2 3 4], variance=[[1 2 3],[2 3 4]]); |