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Added new option for setting weights in CPF algorithm.

time-shift
Stéphane Adjemian (Charybdis) 2016-06-01 16:27:37 +02:00
parent 67b808207c
commit 15f83d0e18
5 changed files with 20 additions and 1 deletions
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9
doc/dynare.texi
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@ -6033,6 +6033,8 @@ Sets the method for approximating the proposal distribution. Possible values for
@anchor{distribution_approximation}
Sets the method for approximating the particle distribution. Possible values for @var{OPTION} are: @code{cubature}, @code{montecarlo} and @code{unscented}. Default value is @code{unscented}.
@item cpf_weights = @var{OPTION}
@anchor{cpf_weights} Controls the method used to update the weights in conditional particle filter, possible values are @code{amisanotristani} (@cite{Amisano et al (2010)}) or @code{murrayjonesparslow} (@cite{Murray et al. (2013)}). Default value is @code{amisanotristani}.
@end table
@ -13551,6 +13553,9 @@ Adjemian, Stéphane, Matthieu Darracq Parriès and Stéphane Moyen (2008): ``Tow
Aguiar, Mark and Gopinath, Gita (2004): ``Emerging Market Business
Cycles: The Cycle is the Trend,'' @i{NBER Working Paper}, 10734
@item
Amisano, Gianni and Tristani, Oreste (2010): ``Euro area inflation persistence in an estimated nonlinear DSGE model'', @i{Journal of Economic Dynamics and Control}, 34(10), 1837--1858
@item
Andreasen, Martin M., Jesús Fernández-Villaverde, and Juan Rubio-Ramírez (2013): ``The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications,'' @i{NBER Working Paper}, 18983
@ -13742,6 +13747,10 @@ of Monetary Economics}, 54(4), 1069--1087
Mancini-Griffoli, Tommaso (2007): ``Dynare User Guide: An introduction
to the solution and estimation of DSGE models''
@item
Murray, Lawrence M., Emlyn M. Jones and John Parslow (2013): ``On Disturbance State-Space Models and the Particle Marginal
Metropolis-Hastings Sampler'', @i{SIAM/ASA Journal on Uncertainty Quantification}, 1, 494–521.
@item
Pearlman, Joseph, David Currie, and Paul Levine (1986): ``Rational
expectations models with partial information,'' @i{Economic

3
matlab/global_initialization.m
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@ -282,6 +282,9 @@ particle.mixture_measurement_shocks = 1 ;
% Online approach
particle.liu_west_delta = 0.99 ;
particle.liu_west_chol_sigma_bar = .01 ;
% Options for setting the weights in conditional particle filters.
particle.cpf_weights_method.amisanotristani = 1;
particle.cpf_weights_method.murrayjonesparslow = 0;
% Copy ep structure in options_ global structure
options_.particle = particle;
options_.rwgmh.init_scale = 1e-4 ;

2
matlab/particles

@ -1 +1 @@
Subproject commit a73e4d94cb792f439a449b54cd5493a386c7bb9e
Subproject commit af66ea9c287d84302783d464f348f787d08d3f08

4
preprocessor/DynareBison.yy
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@ -109,6 +109,7 @@ class ParsingDriver;
%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
%token MODEL_DIAGNOSTICS MODIFIEDHARMONICMEAN MOMENTS_VARENDO CONTEMPORANEOUS_CORRELATION DIFFUSE_FILTER SUB_DRAWS TAPER_STEPS GEWEKE_INTERVAL MCMC_JUMPING_COVARIANCE MOMENT_CALIBRATION
%token NUMBER_OF_PARTICLES RESAMPLING SYSTEMATIC GENERIC RESAMPLING_THRESHOLD RESAMPLING_METHOD KITAGAWA STRATIFIED SMOOTH
%token CPF_WEIGHTS AMISANOTRISTANI MURRAYJONESPARSLOW
%token FILTER_ALGORITHM PROPOSAL_APPROXIMATION CUBATURE UNSCENTED MONTECARLO DISTRIBUTION_APPROXIMATION
%token <string_val> NAME
%token NAN_CONSTANT NO_STATIC NOBS NOCONSTANT NODISPLAY NOCORR NODIAGNOSTIC NOFUNCTIONS NO_HOMOTOPY
@ -1773,6 +1774,7 @@ estimation_options : o_datafile
| o_resampling_threshold
| o_resampling_method
| o_filter_algorithm
| o_cpf_weights
| o_proposal_approximation
| o_distribution_approximation
| o_dirname
@ -2886,6 +2888,8 @@ o_resampling_threshold : RESAMPLING_THRESHOLD EQUAL non_negative_number { driver
o_resampling_method : RESAMPLING_METHOD EQUAL KITAGAWA {driver.option_num("particle.resampling.method.kitagawa", "1"); driver.option_num("particle.resampling.method.smooth", "0"); driver.option_num("particle.resampling.smethod.stratified", "0"); }
| RESAMPLING_METHOD EQUAL SMOOTH {driver.option_num("particle.resampling.method.kitagawa", "0"); driver.option_num("particle.resampling.method.smooth", "1"); driver.option_num("particle.resampling.smethod.stratified", "0"); }
| RESAMPLING_METHOD EQUAL STRATIFIED {driver.option_num("particle.resampling.method.kitagawa", "0"); driver.option_num("particle.resampling.method.smooth", "0"); driver.option_num("particle.resampling.method.stratified", "1"); };
o_cpf_weights : CPF_WEIGHTS EQUAL AMISANOTRISTANI {driver.option_num("particle.cpf_weights_method.amisanotristani", "1"); driver.option_num("particle.cpf_weights_method.murrayjonesparslow", "0"); }
| CPF_WEIGHTS EQUAL MURRAYJONESPARSLOW {driver.option_num("particle.cpf_weights_method.amisanotristani", "0"); driver.option_num("particle.cpf_weights_method.murrayjonesparslow", "1"); };
o_filter_algorithm : FILTER_ALGORITHM EQUAL symbol { driver.option_str("particle.filter_algorithm", $3); };
o_proposal_approximation : PROPOSAL_APPROXIMATION EQUAL CUBATURE {driver.option_num("particle.proposal_approximation.cubature", "1"); driver.option_num("particle.proposal_approximation.unscented", "0"); driver.option_num("particle.proposal_approximation.montecarlo", "0");}
| PROPOSAL_APPROXIMATION EQUAL UNSCENTED {driver.option_num("particle.proposal_approximation.cubature", "0"); driver.option_num("particle.proposal_approximation.unscented", "1"); driver.option_num("particle.proposal_approximation.montecarlo", "0");}

3
preprocessor/DynareFlex.ll
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@ -383,6 +383,9 @@ 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>kitagawa {return token::KITAGAWA;}
<DYNARE_STATEMENT>smooth {return token::SMOOTH;}
<DYNARE_STATEMENT>stratified {return token::STRATIFIED;}
<DYNARE_STATEMENT>cpf_weights {return token::CPF_WEIGHTS;}
<DYNARE_STATEMENT>amisanotristani {return token::AMISANOTRISTANI;}
<DYNARE_STATEMENT>murrayjonesparslow {return token::MURRAYJONESPARSLOW;}
<DYNARE_STATEMENT>filter_algorithm {return token::FILTER_ALGORITHM;}
<DYNARE_STATEMENT>proposal_approximation {return token::PROPOSAL_APPROXIMATION;}
<DYNARE_STATEMENT>cubature {return token::CUBATURE;}