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New option linear_approximation for perfect foresight models. 

This approach only requires one evaluation of the dynamic model (and its
jacobian) instead of T (the number of perdiods). Also (because the model
is linear) the equilibrium paths are obtained by inverting the jacobian
of the stacked equations (no need for a Newton algorithm).

Only available with stack_solve_algo==0 (which is the default algorithm
for solving perfect foresight models).

If possible, the option is triggered automatically if the model is
declared linear.

TODO:
 * Write a linear version of perfect_foresight_problem routine.
 * Evaluate the approxilation error (just need to evaluate the system of
 stacked non linear equations).
issue#70
Stéphane Adjemian (Charybdis) 2015-07-05 21:57:29 +02:00
parent 663d7d82bb
commit 6e52dfb828
2 changed files with 4 additions and 1 deletions
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4
DynareBison.yy
View File

@ -102,7 +102,7 @@ class ParsingDriver;
%token <string_val> INT_NUMBER
%token INV_GAMMA_PDF INV_GAMMA1_PDF INV_GAMMA2_PDF IRF IRF_SHOCKS IRF_PLOT_THRESHOLD IRF_CALIBRATION
%token KALMAN_ALGO KALMAN_TOL DIFFUSE_KALMAN_TOL SUBSAMPLES OPTIONS TOLF
%token LAPLACE LIK_ALGO LIK_INIT LINEAR LOAD_IDENT_FILES LOAD_MH_FILE LOAD_PARAMS_AND_STEADY_STATE LOGLINEAR LOGDATA LYAPUNOV
%token LAPLACE LIK_ALGO LIK_INIT LINEAR LOAD_IDENT_FILES LOAD_MH_FILE LOAD_PARAMS_AND_STEADY_STATE LOGLINEAR LOGDATA LYAPUNOV LINEAR_APPROXIMATION
%token LYAPUNOV_FIXED_POINT_TOL LYAPUNOV_DOUBLING_TOL LYAPUNOV_SQUARE_ROOT_SOLVER_TOL LOG_DEFLATOR LOG_TREND_VAR LOG_GROWTH_FACTOR MARKOWITZ MARGINAL_DENSITY MAX MAXIT
%token MFS MH_CONF_SIG MH_DROP MH_INIT_SCALE MH_JSCALE MH_MODE MH_NBLOCKS MH_REPLIC MH_RECOVER POSTERIOR_MAX_SUBSAMPLE_DRAWS MIN MINIMAL_SOLVING_PERIODS
%token MODE_CHECK MODE_CHECK_NEIGHBOURHOOD_SIZE MODE_CHECK_SYMMETRIC_PLOTS MODE_CHECK_NUMBER_OF_POINTS MODE_COMPUTE MODE_FILE MODEL MODEL_COMPARISON MODEL_INFO MSHOCKS ABS SIGN
@ -993,6 +993,7 @@ perfect_foresight_setup_options_list : perfect_foresight_setup_options_list COMM
;
perfect_foresight_setup_options : o_periods
| o_linear_approximation
| o_datafile
;
@ -2675,6 +2676,7 @@ o_mh_mode : MH_MODE EQUAL INT_NUMBER { driver.option_num("mh_mode", $3); };
o_mh_nblocks : MH_NBLOCKS EQUAL INT_NUMBER { driver.option_num("mh_nblck", $3); };
o_load_mh_file : LOAD_MH_FILE { driver.option_num("load_mh_file", "1"); };
o_loglinear : LOGLINEAR { driver.option_num("loglinear", "1"); };
o_linear_approximation : LINEAR_APPROXIMATION { driver.option_num("linear_approximation", "1"); };
o_logdata : LOGDATA { driver.option_num("logdata", "1"); };
o_nodiagnostic : NODIAGNOSTIC { driver.option_num("nodiagnostic", "1"); };
o_bayesian_irf : BAYESIAN_IRF { driver.option_num("bayesian_irf", "1"); };

1
DynareFlex.ll
View File

@ -286,6 +286,7 @@ DATE -?[0-9]+([YyAa]|[Mm]([1-9]|1[0-2])|[Qq][1-4]|[Ww]([1-9]{1}|[1-4][0-9]|5[0-2
<DYNARE_STATEMENT>mh_nblocks {return token::MH_NBLOCKS;}
<DYNARE_STATEMENT>load_mh_file {return token::LOAD_MH_FILE;}
<DYNARE_STATEMENT>loglinear {return token::LOGLINEAR;}
<DYNARE_STATEMENT>linear_approximation {return token::LINEAR_APPROXIMATION;}
<DYNARE_STATEMENT>logdata {return token::LOGDATA;}
<DYNARE_STATEMENT>nodiagnostic {return token::NODIAGNOSTIC;}
<DYNARE_STATEMENT>kalman_algo {return token::KALMAN_ALGO;}