Merge pull request #432 from JohannesPfeifer/osr_crit
Transfer hardcoded osr options to options structuretime-shift
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5288f2a67a
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@ -5521,12 +5521,22 @@ The parameters to be optimized must be listed with @code{osr_params}.
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The quadratic objectives must be listed with @code{optim_weights}.
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This problem is solved using a numerical optimizer.
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This problem is solved using the numerical optimizer @code{csminwel} of Chris Sims.
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@optionshead
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This command accept the same options than @code{stoch_simul}
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(@pxref{Computing the stochastic solution}).
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This command accepts the same options as @code{stoch_simul}
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(@pxref{Computing the stochastic solution}) plus
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@table @code
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@item maxit = = @var{INTEGER}
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Determines the maximum number of iterations used in the non-linear solver. Default: @code{1000}
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@item tolf = = @var{DOUBLE}
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Convergence criterion for termination based on the function value. Iteration will
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cease when it proves impossible to improve the function value by more than tolf. Default: @code{1e-7}
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@end table
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The value of the objective is stored in the variable
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@code{oo_.osr.objective_function}, which is described below.
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@ -565,6 +565,11 @@ options_.risky_steadystate = 0;
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options_.endogenous_prior = 0;
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options_.endogenous_prior_restrictions.irf={};
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% OSR Optimal Simple Rules
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options_.osr.tolf=1e-7;
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options_.osr.maxit=1000;
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options_.osr.verbose=2;
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% use GPU
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options_.gpu = 0;
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@ -48,9 +48,9 @@ t0 = M_.params(i_params);
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inv_order_var = oo_.dr.inv_order_var;
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H0 = 1e-4*eye(np);
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crit = 1e-7;
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nit = 1000;
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verbose = 2;
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crit=options_.osr.tolf;
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nit=options_.osr.maxit;
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verbose=options_.osr.verbose;
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[f,p]=csminwel1('osr_obj',t0,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,i_params,...
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inv_order_var(i_var),weights(i_var,i_var));
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