From 079bf6c60e6fbc5db99759041cf33b739be0f570 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Adjemian=20=28Charybdis=29?= Date: Mon, 29 May 2017 10:41:58 +0200 Subject: [PATCH] Fixed indentation for Markdown. --- NEWS | 842 ++++++++++++++++++++++++++++++----------------------------- 1 file changed, 425 insertions(+), 417 deletions(-) diff --git a/NEWS b/NEWS index 796078725..71e9b45d4 100644 --- a/NEWS +++ b/NEWS @@ -19,367 +19,374 @@ This release is compatible with MATLAB versions ranging from 7.3 (R2006b) to Here is the list of major user-visible changes: + +Dynare 4.5 +========== + + - Ramsey policy - + Added command `ramsey_model` that builds the expanded model with - FOC conditions for the planner's problem but doesn't perform any - computation. Usefull to compute Ramsey policy in a perfect - foresight model, + + Added command `ramsey_model` that builds the expanded model with + FOC conditions for the planner's problem but doesn't perform any + computation. Usefull to compute Ramsey policy in a perfect + foresight model, - + `ramsey_policy` accepts multipliers in its variable list and - displays results for them. + + `ramsey_policy` accepts multipliers in its variable list and + displays results for them. - Perfect foresight models - + New commands `perfect_foresight_setup` (for preparing the - simulation) and `perfect_foresight_solver` (for computing it). The - old `simul` command still exist and is now an alias for - `perfect_foresight_setup` + `perfect_foresight_solver`. It is no - longer possible to manipulate by hand the contents of - `oo_.exo_simul` when using `simul`. People who want to do - it must first call `perfect_foresight_setup`, then do the - manipulations, then call `perfect_foresight_solver`, + + New commands `perfect_foresight_setup` (for preparing the + simulation) and `perfect_foresight_solver` (for computing it). The + old `simul` command still exist and is now an alias for + `perfect_foresight_setup` + `perfect_foresight_solver`. It is no + longer possible to manipulate by hand the contents of + `oo_.exo_simul` when using `simul`. People who want to do + it must first call `perfect_foresight_setup`, then do the + manipulations, then call `perfect_foresight_solver`, - + By default, the perfect foresight solver will try a homotopy - method if it fails to converge at the first try. The old behavior - can be restored with the `no_homotopy` option, + + By default, the perfect foresight solver will try a homotopy + method if it fails to converge at the first try. The old behavior + can be restored with the `no_homotopy` option, - + New option `stack_solve_algo=7` that allows specifying a - `solve_algo` solver for solving the model, + + New option `stack_solve_algo=7` that allows specifying a + `solve_algo` solver for solving the model, - + New option `solve_algo` that allows specifying a solver for - solving the model when using `stack_solve_algo=7`, + + New option `solve_algo` that allows specifying a solver for + solving the model when using `stack_solve_algo=7`, - + New option `lmmcp` that solves the model via a Levenberg-Marquardt - mixed complementarity problem (LMMCP) solver, + + New option `lmmcp` that solves the model via a Levenberg-Marquardt + mixed complementarity problem (LMMCP) solver, - + New option `robust_lin_solve` that triggers the use of a robust - linear solver for the default `solve_algo=4`, + + New option `robust_lin_solve` that triggers the use of a robust + linear solver for the default `solve_algo=4`, - + New options `tolf` and `tolx` to control termination criteria of - solvers, + + New options `tolf` and `tolx` to control termination criteria of + solvers, - + New option `endogenous_terminal_period` to `simul`, + + New option `endogenous_terminal_period` to `simul`, - + Added the possibility to set the initial condition of the - (stochastic) extended path simulations with the histval block. + + Added the possibility to set the initial condition of the + (stochastic) extended path simulations with the histval block. - Optimal simple rules - + Saves the optimal value of parameters to `oo_.osr.optim_params`, + + Saves the optimal value of parameters to `oo_.osr.optim_params`, - + New block `osr_params_bounds` allows specifying bounds for the - estimated parameters, + + New block `osr_params_bounds` allows specifying bounds for the + estimated parameters, - + New option `opt_algo` allows selecting different optimizers while - the new option `optim` allows specifying the optimizer options, + + New option `opt_algo` allows selecting different optimizers while + the new option `optim` allows specifying the optimizer options, - + The `osr` command now saves the names, bounds, and indices for the - estimated parameters as well as the indices and weights of the - variables entering the objective function into `M_.osr`. + + The `osr` command now saves the names, bounds, and indices for the + estimated parameters as well as the indices and weights of the + variables entering the objective function into `M_.osr`. - Forecasts and Smoothing - + The smoother and forecasts take uncertainty about trends and means - into account, + + The smoother and forecasts take uncertainty about trends and means + into account, - + Forecasts accounting for measurement error are now saved in fields - of the form `HPDinf_ME` and `HPDsup_ME`, + + Forecasts accounting for measurement error are now saved in fields + of the form `HPDinf_ME` and `HPDsup_ME`, - + New fields `oo_.Smoother.Trend` and `oo_.Smoother.Constant` that - save the trend and constant parts of the smoothed variables, + + New fields `oo_.Smoother.Trend` and `oo_.Smoother.Constant` that + save the trend and constant parts of the smoothed variables, - + new field `oo_.Smoother.TrendCoeffs` that stores the trend - coefficients. + + new field `oo_.Smoother.TrendCoeffs` that stores the trend + coefficients. - + Rolling window forecasts allowed in `estimation` command by - passing a vector to `first_obs`, + + Rolling window forecasts allowed in `estimation` command by + passing a vector to `first_obs`, - + The `calib_smoother` command now accepts the `loglinear`, - `prefilter`, `first_obs` and `filter_decomposition` options. + + The `calib_smoother` command now accepts the `loglinear`, + `prefilter`, `first_obs` and `filter_decomposition` options. - Estimation - + New options: `logdata`, `consider_all_endogenous`, - `consider_only_observed`, `posterior_max_subsample_draws`, - `mh_conf_sig`, `diffuse_kalman_tol`, `dirname`, `nodecomposition` + + New options: `logdata`, `consider_all_endogenous`, + `consider_only_observed`, `posterior_max_subsample_draws`, + `mh_conf_sig`, `diffuse_kalman_tol`, `dirname`, `nodecomposition` - + `load_mh_file` and `mh_recover` now try to load chain's proposal density, + + `load_mh_file` and `mh_recover` now try to load chain's proposal density, - + New option `load_results_after_load_mh` that allows loading some - posterior results from a previous run if no new MCMC draws are - added, + + New option `load_results_after_load_mh` that allows loading some + posterior results from a previous run if no new MCMC draws are + added, - + New option `posterior_nograph` that suppresses the generation of - graphs associated with Bayesian IRFs, posterior smoothed objects, - and posterior forecasts, + + New option `posterior_nograph` that suppresses the generation of + graphs associated with Bayesian IRFs, posterior smoothed objects, + and posterior forecasts, - + Saves the posterior density at the mode in - `oo_.posterior.optimization.log_density`, + + Saves the posterior density at the mode in + `oo_.posterior.optimization.log_density`, - + The `filter_covariance` option now also works with posterior - sampling like Metropolis-Hastings, + + The `filter_covariance` option now also works with posterior + sampling like Metropolis-Hastings, - + New option `no_posterior_kernel_density` to suppress computation - of kernel density of posterior objects, + + New option `no_posterior_kernel_density` to suppress computation + of kernel density of posterior objects, - + Recursive estimation and forecasting now provides the individual - `oo_` structures for each sample in `oo_recursive_`, + + Recursive estimation and forecasting now provides the individual + `oo_` structures for each sample in `oo_recursive_`, - + The `trace_plot` command can now plot the posterior density, + + The `trace_plot` command can now plot the posterior density, - + New command `generate_trace_plots` allows generating all trace - plots for one chain, + + New command `generate_trace_plots` allows generating all trace + plots for one chain, - + New commands `prior_function` and `posterior_function` that - execute a user-defined function on parameter draws from the - prior/posterior distribution, + + New commands `prior_function` and `posterior_function` that + execute a user-defined function on parameter draws from the + prior/posterior distribution, - + New option `huge_number` for replacement of infinite bounds with - large number during `mode_compute`, + + New option `huge_number` for replacement of infinite bounds with + large number during `mode_compute`, - + New option `posterior_sampling_method` allows selecting the new - posterior sampling options: - `tailored_random_block_metropolis_hastings` (Tailored randomized - block (TaRB) Metropolis-Hastings), `slice` (Slice sampler), - `independent_metropolis_hastings` (Independent - Metropolis-Hastings), + + New option `posterior_sampling_method` allows selecting the new + posterior sampling options: + `tailored_random_block_metropolis_hastings` (Tailored randomized + block (TaRB) Metropolis-Hastings), `slice` (Slice sampler), + `independent_metropolis_hastings` (Independent + Metropolis-Hastings), - + New option `posterior_sampler_options` that allow controlling the - options of the `posterior_sampling_method`, its `scale_file`-option - pair allows loading the `_mh_scale.mat`-file storing the tuned - scale factor from a previous run of `mode_compute=6`, + + New option `posterior_sampler_options` that allow controlling the + options of the `posterior_sampling_method`, its `scale_file`-option + pair allows loading the `_mh_scale.mat`-file storing the tuned + scale factor from a previous run of `mode_compute=6`, - + New option `raftery_lewis_diagnostics` that computes Raftery/Lewis - (1992) convergence diagnostics, + + New option `raftery_lewis_diagnostics` that computes Raftery/Lewis + (1992) convergence diagnostics, - + New option `fast_kalman_filter` that provides fast Kalman filter - using Chandrasekhar recursions as described in Ed Herbst (2015), + + New option `fast_kalman_filter` that provides fast Kalman filter + using Chandrasekhar recursions as described in Ed Herbst (2015), - + The `dsge_var` option now saves results at the posterior mode into - `oo_.dsge_var`, + + The `dsge_var` option now saves results at the posterior mode into + `oo_.dsge_var`, - + New option `smoothed_state_uncertainty` to provide the uncertainty - estimate for the smoothed state estimate from the Kalman smoother, + + New option `smoothed_state_uncertainty` to provide the uncertainty + estimate for the smoothed state estimate from the Kalman smoother, - + New prior density: generalized Weibull distribution, + + New prior density: generalized Weibull distribution, - + Option `mh_recover` now allows continuing a crashed chain at the - last save mh-file, + + Option `mh_recover` now allows continuing a crashed chain at the + last save mh-file, - + New option `nonlinear_filter_initialization` for the - {{{estimation}}} command. Controls the initial covariance matrix - of the state variables in nonlinear filters. + + New option `nonlinear_filter_initialization` for the + `estimation` command. Controls the initial covariance matrix + of the state variables in nonlinear filters. - + The `conditional_variance_decomposition` option now displays - output and stores it as a LaTeX-table when the `TeX` option is - invoked, + + The `conditional_variance_decomposition` option now displays + output and stores it as a LaTeX-table when the `TeX` option is + invoked, - + The `use_calibration` to `estimated_params_init` now also works - with ML, + + The `use_calibration` to `estimated_params_init` now also works + with ML, - + Improved initial estimation checks. + + Improved initial estimation checks. - Steady state - + The default solver for finding the steady state is now a - trust-region solver (can be triggered explicitly with option - `solve_algo=4`), + + The default solver for finding the steady state is now a + trust-region solver (can be triggered explicitly with option + `solve_algo=4`), - + New options `tolf` and `tolx` to control termination criteria of - solver, + + New options `tolf` and `tolx` to control termination criteria of + solver, - + The debugging mode now provides the termination values in steady - state finding. + + The debugging mode now provides the termination values in steady + state finding. - Stochastic simulations - + New options `nodecomposition`, + + New options `nodecomposition`, - + New option `bandpass_filter` to compute bandpass-filtered - theoretical and simulated moments, + + New option `bandpass_filter` to compute bandpass-filtered + theoretical and simulated moments, - + New option `one_sided_hp_filter` to compute one-sided HP-filtered - simulated moments, + + New option `one_sided_hp_filter` to compute one-sided HP-filtered + simulated moments, - + `stoch_simul` displays a simulated variance decomposition when - simulated moments are requested, + + `stoch_simul` displays a simulated variance decomposition when + simulated moments are requested, - + `stoch_simul` saves skewness and kurtosis into respective fields - of `oo_` when simulated moments have been requested, + + `stoch_simul` saves skewness and kurtosis into respective fields + of `oo_` when simulated moments have been requested, - + `stoch_simul` saves the unconditional variance decomposition in - `oo_.variance_decomposition`, + + `stoch_simul` saves the unconditional variance decomposition in + `oo_.variance_decomposition`, - + New option `dr_display_tol` that governs omission of small terms - in display of decision rules, + + New option `dr_display_tol` that governs omission of small terms + in display of decision rules, - + The `stoch_simul` command now prints the displayed tables as LaTeX - code when the new `TeX` option is enabled, + + The `stoch_simul` command now prints the displayed tables as LaTeX + code when the new `TeX` option is enabled, - + The `loglinear` option now works with lagged and leaded exogenous - variables like news shocks, + + The `loglinear` option now works with lagged and leaded exogenous + variables like news shocks, - + New option `spectral_density` that allows displaying the spectral - density of (filtered) endogenous variables, + + New option `spectral_density` that allows displaying the spectral + density of (filtered) endogenous variables, - + New option `contemporaneous_correlation` that allows saving - contemporaneous correlations in addition to the covariances. + + New option `contemporaneous_correlation` that allows saving + contemporaneous correlations in addition to the covariances. - Identification - + New options `diffuse_filter` and `prior_trunc`, + + New options `diffuse_filter` and `prior_trunc`, - + The `identification` command now supports correlations via - simulated moments, + + The `identification` command now supports correlations via + simulated moments, - Sensitivity analysis - + New blocks `irf_calibration` and `moment_calibration`, + + New blocks `irf_calibration` and `moment_calibration`, - + Outputs LaTeX tables if the new `TeX` option is used, + + Outputs LaTeX tables if the new `TeX` option is used, - + New option `relative_irf` to `irf_calibration` block. + + New option `relative_irf` to `irf_calibration` block. - Conditional forecast - + Command `conditional_forecast` now takes into account `histval` - block if present. + + Command `conditional_forecast` now takes into account `histval` + block if present. - Shock decomposition - + New option `colormap` to `shocks_decomposition` for controlling - the color map used in the shocks decomposition graphs, + + New option `colormap` to `shocks_decomposition` for controlling + the color map used in the shocks decomposition graphs, - + `shocks_decomposition` now accepts the `nograph` option, + + `shocks_decomposition` now accepts the `nograph` option, - + New command `realtime_shock_decomposition` that for each period `T= [presample,...,nobs]` - allows computing the: + + New command `realtime_shock_decomposition` that for each period `T= [presample,...,nobs]` + allows computing the: - o realtime historical shock decomposition `Y(t|T)`, i.e. without observing data in `[T+1,...,nobs]` - o forecast shock decomposition `Y(T+k|T)` - o realtime conditional shock decomposition `Y(T+k|T+k)-Y(T+k|T)` + * realtime historical shock decomposition `Y(t|T)`, i.e. without observing data in `[T+1,...,nobs]` - + New block `shock_groups` that allows grouping shocks for the - `shock_decomposition` and `realtime_shock_decomposition` commands, + * forecast shock decomposition `Y(T+k|T)` - + New command `plot_shock_decomposition` that allows plotting the - results from `shock_decomposition` and - `realtime_shock_decomposition` for different vintages and shock - groupings. + * realtime conditional shock decomposition `Y(T+k|T+k)-Y(T+k|T)` + + + New block `shock_groups` that allows grouping shocks for the + `shock_decomposition` and `realtime_shock_decomposition` commands, + + + New command `plot_shock_decomposition` that allows plotting the + results from `shock_decomposition` and + `realtime_shock_decomposition` for different vintages and shock + groupings. - Macroprocessor - + Can now pass a macro-variable to the `@#include` macro directive, + + Can now pass a macro-variable to the `@#include` macro directive, - + New preprocessor flag `-I`, macro directive `@#includepath`, and - dynare config file block `[paths]` to pass a search path to the - macroprocessor to be used for file inclusion via `@#include`. + + New preprocessor flag `-I`, macro directive `@#includepath`, and + dynare config file block `[paths]` to pass a search path to the + macroprocessor to be used for file inclusion via `@#include`. - Command line - + New option `onlyclearglobals` (do not clear JIT compiled functions - with recent versions of Matlab), + + New option `onlyclearglobals` (do not clear JIT compiled functions + with recent versions of Matlab), - + New option `minimal_workspace` to use fewer variables in the - current workspace, + + New option `minimal_workspace` to use fewer variables in the + current workspace, - + New option `params_derivs_order` allows limiting the order of the - derivatives with respect to the parameters that are calculated by - the preprocessor, + + New option `params_derivs_order` allows limiting the order of the + derivatives with respect to the parameters that are calculated by + the preprocessor, - + New command line option `mingw` to support the MinGW-w64 C/C++ - Compiler from TDM-GCC for `use_dll`. + + New command line option `mingw` to support the MinGW-w64 C/C++ + Compiler from TDM-GCC for `use_dll`. - dates/dseries/reporting classes - + New methods `abs`, `cumprod` and `chain`, + + New methods `abs`, `cumprod` and `chain`, - + New option `tableRowIndent` to `addTable`, + + New option `tableRowIndent` to `addTable`, - + Reporting system revamped and made more efficient, dependency on - matlab2tikz has been dropped. + + Reporting system revamped and made more efficient, dependency on + matlab2tikz has been dropped. - Optimization algorithms - + `mode_compute=2` Uses the simulated annealing as described by - Corana et al. (1987), + + `mode_compute=2` Uses the simulated annealing as described by + Corana et al. (1987), - + `mode_compute=101` Uses SOLVEOPT as described by Kuntsevich and - Kappel (1997), + + `mode_compute=101` Uses SOLVEOPT as described by Kuntsevich and + Kappel (1997), - + `mode_compute=102` Uses `simulannealbnd` from Matlab's Global - Optimization Toolbox (if available), + + `mode_compute=102` Uses `simulannealbnd` from Matlab's Global + Optimization Toolbox (if available), - + New option `silent_optimizer` to shut off output from mode - computing/optimization, + + New option `silent_optimizer` to shut off output from mode + computing/optimization, - + New options `verbosity` and `SaveFiles` to control output and - saving of files during mode computing/optimization. + + New options `verbosity` and `SaveFiles` to control output and + saving of files during mode computing/optimization. - LaTeX output - + New command `write_latex_original_model`, + + New command `write_latex_original_model`, - + New option `write_equation_tags` to `write_latex_dynamic_model` - that allows printing the specified equation tags to the generate - LaTeX code, + + New option `write_equation_tags` to `write_latex_dynamic_model` + that allows printing the specified equation tags to the generate + LaTeX code, - + New command `write_latex_parameter_table` that writes the names and - values of model parameters to a LaTeX table, + + New command `write_latex_parameter_table` that writes the names and + values of model parameters to a LaTeX table, - + New command `write_latex_prior_table` that writes the descriptive - statistics about the prior distribution to a LaTeX table, + + New command `write_latex_prior_table` that writes the descriptive + statistics about the prior distribution to a LaTeX table, - + New command `collect_latex_files` that creates one compilable LaTeX - file containing all TeX-output. + + New command `collect_latex_files` that creates one compilable LaTeX + file containing all TeX-output. - Misc. - + Provides 64bit preprocessor, + + Provides 64bit preprocessor, - + Introduces new path management to avoid conflicts with other - toolboxes, + + Introduces new path management to avoid conflicts with other + toolboxes, - + Full compatibility with Matlab 2014b's new graphic interface, + + Full compatibility with Matlab 2014b's new graphic interface, - + When using `model(linear)`, Dynare automatically checks - whether the model is truly linear, + + When using `model(linear)`, Dynare automatically checks + whether the model is truly linear, - + `usedll`, the `msvc` option now supports `normcdf`, `acosh`, - `asinh`, and `atanh`, + + `usedll`, the `msvc` option now supports `normcdf`, `acosh`, + `asinh`, and `atanh`, - + New parallel option `NumberOfThreadsPerJob` for Windows nodes that - sets the number of threads assigned to each remote MATLAB/Octave - run, + + New parallel option `NumberOfThreadsPerJob` for Windows nodes that + sets the number of threads assigned to each remote MATLAB/Octave + run, - + Improved numerical performance of - `schur_statespace_transformation` for very large models, + + Improved numerical performance of + `schur_statespace_transformation` for very large models, - + The `all_values_required` option now also works with `histval`, + + The `all_values_required` option now also works with `histval`, - + Add missing `horizon` option to `ms_forecast`, + + Add missing `horizon` option to `ms_forecast`, - + BVAR now saves the marginal data density in - `oo_.bvar.log_marginal_data_density` and stores prior and - posterior information in `oo_.bvar.prior` and - `oo_.bvar.posterior`. + + BVAR now saves the marginal data density in + `oo_.bvar.log_marginal_data_density` and stores prior and + posterior information in `oo_.bvar.prior` and + `oo_.bvar.posterior`. @@ -388,355 +395,355 @@ Here is the list of major user-visible changes: - BVAR models - + `bvar_irf` could display IRFs in an unreadable way when they moved from - negative to positive values, + + `bvar_irf` could display IRFs in an unreadable way when they moved from + negative to positive values, - + In contrast to what is stated in the documentation, the confidence interval - size `conf_sig` was 0.6 by default instead of 0.9. + + In contrast to what is stated in the documentation, the confidence interval + size `conf_sig` was 0.6 by default instead of 0.9. - Conditional forecasts - + The `conditional_forecast` command produced wrong results in calibrated - models when used at initial values outside of the steady state (given with - `initval`), + + The `conditional_forecast` command produced wrong results in calibrated + models when used at initial values outside of the steady state (given with + `initval`), - + The `plot_conditional_forecast` option could produce unreadable figures if - the areas overlap, + + The `plot_conditional_forecast` option could produce unreadable figures if + the areas overlap, - + The `conditional_forecast` command after MLE crashed, + + The `conditional_forecast` command after MLE crashed, - + In contrast to what is stated in the manual, the confidence interval size - `conf_sig` was 0.6 by default instead of 0.8. + + In contrast to what is stated in the manual, the confidence interval size + `conf_sig` was 0.6 by default instead of 0.8. - + Conditional forecasts were wrong when the declaration of endogenous - variables was not preceeding the declaration of the exogenous - variables and parameters. + + Conditional forecasts were wrong when the declaration of endogenous + variables was not preceeding the declaration of the exogenous + variables and parameters. - Discretionary policy - + Dynare allowed running models where the number of instruments did not match - the number of omitted equations, + + Dynare allowed running models where the number of instruments did not match + the number of omitted equations, - + Dynare could crash in some cases when trying to display the solution, + + Dynare could crash in some cases when trying to display the solution, - + Parameter dependence embedded via a `steady_state` was not taken into - account, typically resulting in crashes. + + Parameter dependence embedded via a `steady_state` was not taken into + account, typically resulting in crashes. - dseries class - + When subtracting a dseries object from a number, the number was instead - subtracted from the dseries object. + + When subtracting a dseries object from a number, the number was instead + subtracted from the dseries object. - DSGE-VAR models - + Dynare crashed when estimation encountered non-finite values in the Jacobian - at the steady state, + + Dynare crashed when estimation encountered non-finite values in the Jacobian + at the steady state, - + The presence of a constant was not considered for degrees of freedom - computation of the Gamma function used during the posterior computation; due - to only affecting the constant term, results should be be unaffected, except - for model_comparison when comparing models with and without. + + The presence of a constant was not considered for degrees of freedom + computation of the Gamma function used during the posterior computation; due + to only affecting the constant term, results should be be unaffected, except + for model_comparison when comparing models with and without. - Estimation command - + In contrast to what was stated in the manual, the confidence interval size - `conf_sig` for `forecast` without MCMC was 0.6 by default instead of 0.9, + + In contrast to what was stated in the manual, the confidence interval size + `conf_sig` for `forecast` without MCMC was 0.6 by default instead of 0.9, - + Calling estimation after identification could lead to crashes, + + Calling estimation after identification could lead to crashes, - + When using recursive estimation/forecasting and setting some elements of - `nobs` to be larger than the number of observations T in the data, - `oo_recursive_` contained additional cell entries that simply repeated the - results obtained for `oo_recursive_T`, + + When using recursive estimation/forecasting and setting some elements of + `nobs` to be larger than the number of observations T in the data, + `oo_recursive_` contained additional cell entries that simply repeated the + results obtained for `oo_recursive_T`, - + Computation of Bayesian smoother could crash for larger models when - requesting `forecast` or `filtered_variables`, + + Computation of Bayesian smoother could crash for larger models when + requesting `forecast` or `filtered_variables`, - + Geweke convergence diagnostics were not computed on the full MCMC chain when - the `load_mh_file` option was used, + + Geweke convergence diagnostics were not computed on the full MCMC chain when + the `load_mh_file` option was used, - + The Geweke convergence diagnostics always used the default `taper_steps` and - `geweke_interval`, + + The Geweke convergence diagnostics always used the default `taper_steps` and + `geweke_interval`, - + Bayesian IRFs (`bayesian_irfs` option) could be displayed in an unreadable - way when they move from negative to positive values, + + Bayesian IRFs (`bayesian_irfs` option) could be displayed in an unreadable + way when they move from negative to positive values, - + If `bayesian_irfs` was requested when `mh_replic` was too low to compute - HPDIs, plotting was crashing, + + If `bayesian_irfs` was requested when `mh_replic` was too low to compute + HPDIs, plotting was crashing, - + The x-axis value in `oo_.prior_density` for the standard deviation and - correlation of measurement errors was written into a field - `mearsurement_errors_*` instead of `measurement_errors_*`, + + The x-axis value in `oo_.prior_density` for the standard deviation and + correlation of measurement errors was written into a field + `mearsurement_errors_*` instead of `measurement_errors_*`, - + Using a user-defined `mode_compute` crashed estimation, + + Using a user-defined `mode_compute` crashed estimation, - + Option `mode_compute=10` did not work with infinite prior bounds, + + Option `mode_compute=10` did not work with infinite prior bounds, - + The posterior variances and covariances computed by `moments_varendo` were - wrong for very large models due to a matrix erroneously being filled up with - zeros, + + The posterior variances and covariances computed by `moments_varendo` were + wrong for very large models due to a matrix erroneously being filled up with + zeros, - + Using the `forecast` option with `loglinear` erroneously added the unlogged - steady state, + + Using the `forecast` option with `loglinear` erroneously added the unlogged + steady state, - + When using the `loglinear` option the check for the presence of a constant - was erroneously based on the unlogged steady state, + + When using the `loglinear` option the check for the presence of a constant + was erroneously based on the unlogged steady state, - + Estimation of `observation_trends` was broken as the trends specified as a - function of deep parameters were not correctly updated during estimation, + + Estimation of `observation_trends` was broken as the trends specified as a + function of deep parameters were not correctly updated during estimation, - + When using `analytic_derivation`, the parameter values were not set before - testing whether the steady state file changes parameter values, leading to - subsequent crashes, + + When using `analytic_derivation`, the parameter values were not set before + testing whether the steady state file changes parameter values, leading to + subsequent crashes, - + If the steady state of an initial parameterization did not solve, the - observation equation could erroneously feature no constant when the - `use_calibration` option was used, + + If the steady state of an initial parameterization did not solve, the + observation equation could erroneously feature no constant when the + `use_calibration` option was used, - + When computing posterior moments, Dynare falsely displayed that moment - computations are skipped, although the computation was performed correctly, + + When computing posterior moments, Dynare falsely displayed that moment + computations are skipped, although the computation was performed correctly, - + If `conditional_variance_decomposition` was requested, although all - variables contain unit roots, Dynare crashed instead of providing an error - message, + + If `conditional_variance_decomposition` was requested, although all + variables contain unit roots, Dynare crashed instead of providing an error + message, - + Computation of the posterior parameter distribution was erroneously based - on more draws than specified (there was one additional draw for every Markov - chain), + + Computation of the posterior parameter distribution was erroneously based + on more draws than specified (there was one additional draw for every Markov + chain), - + The estimation option `lyapunov=fixed_point` was broken, + + The estimation option `lyapunov=fixed_point` was broken, - + Computation of `filtered_vars` with only one requested step crashed Dynare, + + Computation of `filtered_vars` with only one requested step crashed Dynare, - + Option `kalman_algo=3` was broken with non-diagonal measurement error, + + Option `kalman_algo=3` was broken with non-diagonal measurement error, - + When using the diffuse Kalman filter with missing observations, an additive - factor log(2*pi) was missing in the last iteration step, + + When using the diffuse Kalman filter with missing observations, an additive + factor log(2*pi) was missing in the last iteration step, - + Passing of the `MaxFunEvals` and `InitialSimplexSize` options to - `mode_compute=8` was broken, + + Passing of the `MaxFunEvals` and `InitialSimplexSize` options to + `mode_compute=8` was broken, - + Bayesian forecasts contained initial conditions and had the wrong length in - both plots and stored variables, + + Bayesian forecasts contained initial conditions and had the wrong length in + both plots and stored variables, - + Filtered variables obtained with `mh_replic=0`, ML, or - `calibrated_smoother` were padded with zeros at the beginning and end and - had the wrong length in stored variables, + + Filtered variables obtained with `mh_replic=0`, ML, or + `calibrated_smoother` were padded with zeros at the beginning and end and + had the wrong length in stored variables, - + Computation of smoothed measurement errors in Bayesian estimation was broken, + + Computation of smoothed measurement errors in Bayesian estimation was broken, - + The `selected_variables_only` option (`mh_replic=0`, ML, or - `calibrated_smoother`) returned wrong results for smoothed, updated, and - filtered variables, + + The `selected_variables_only` option (`mh_replic=0`, ML, or + `calibrated_smoother`) returned wrong results for smoothed, updated, and + filtered variables, - + Combining the `selected_variables_only` option with forecasts obtained - using `mh_replic=0`, ML, or `calibrated_smoother` leaded to crashes, + + Combining the `selected_variables_only` option with forecasts obtained + using `mh_replic=0`, ML, or `calibrated_smoother` leaded to crashes, - + `oo_.UpdatedVariables` was only filled when the `filtered_vars` option was specified, + + `oo_.UpdatedVariables` was only filled when the `filtered_vars` option was specified, - + When using Bayesian estimation with `filtered_vars`, but without - `smoother`, then `oo_.FilteredVariables` erroneously also contained filtered - variables at the posterior mean as with `mh_replic=0`, + + When using Bayesian estimation with `filtered_vars`, but without + `smoother`, then `oo_.FilteredVariables` erroneously also contained filtered + variables at the posterior mean as with `mh_replic=0`, - + Running an MCMC a second time in the same folder with a different number of - iterations could result in crashes due to the loading of stale files, + + Running an MCMC a second time in the same folder with a different number of + iterations could result in crashes due to the loading of stale files, - + Results displayed after Bayesian estimation when not specifying - the `smoother` option were based on the parameters at the mode - from mode finding instead of the mean parameters from the - posterior draws. This affected the smoother results displayed, but - also calls to subsequent command relying on the parameters stored - in `M_.params` like `stoch_simul`, + + Results displayed after Bayesian estimation when not specifying + the `smoother` option were based on the parameters at the mode + from mode finding instead of the mean parameters from the + posterior draws. This affected the smoother results displayed, but + also calls to subsequent command relying on the parameters stored + in `M_.params` like `stoch_simul`, - + The content of `oo_.posterior_std` after Bayesian estimation was based on - the standard deviation at the posterior mode, not the one from the MCMC, this - was not consistent with the reference manual, + + The content of `oo_.posterior_std` after Bayesian estimation was based on + the standard deviation at the posterior mode, not the one from the MCMC, this + was not consistent with the reference manual, - + When the initialization of an MCMC run failed, the metropolis.log file was - locked, requiring a restart of Matlab to restart estimation, + + When the initialization of an MCMC run failed, the metropolis.log file was + locked, requiring a restart of Matlab to restart estimation, - + If the posterior mode was right at the corner of the prior bounds, the - initialization of the MCMC erroneously crashed, + + If the posterior mode was right at the corner of the prior bounds, the + initialization of the MCMC erroneously crashed, - + If the number of dropped draws via `mh_drop` coincided with the number of - draws in a `_mh'-file`, `oo_.posterior.metropolis.mean` and - `oo_.posterior.metropolis.Variance` were NaN. + + If the number of dropped draws via `mh_drop` coincided with the number of + draws in a `_mh'-file`, `oo_.posterior.metropolis.mean` and + `oo_.posterior.metropolis.Variance` were NaN. - Estimation and calibrated smoother - + When using `observation_trends` with the `prefilter` option, the mean shift - due to the trend was not accounted for, + + When using `observation_trends` with the `prefilter` option, the mean shift + due to the trend was not accounted for, - + When using `first_obs`>1, the higher trend starting point of - `observation_trends` was not taken into account, leading, among other things, - to problems in recursive forecasting, + + When using `first_obs`>1, the higher trend starting point of + `observation_trends` was not taken into account, leading, among other things, + to problems in recursive forecasting, - + The diffuse Kalman smoother was crashing if the forecast error variance - matrix becomes singular, + + The diffuse Kalman smoother was crashing if the forecast error variance + matrix becomes singular, - + The multivariate Kalman smoother provided incorrect state estimates when - all data for one observation are missing, + + The multivariate Kalman smoother provided incorrect state estimates when + all data for one observation are missing, - + The multivariate diffuse Kalman smoother provided incorrect state estimates - when the `Finf` matrix becomes singular, + + The multivariate diffuse Kalman smoother provided incorrect state estimates + when the `Finf` matrix becomes singular, - + The univariate diffuse Kalman filter was crashing if the initial covariance - matrix of the nonstationary state vector is singular, + + The univariate diffuse Kalman filter was crashing if the initial covariance + matrix of the nonstationary state vector is singular, - Forecats - + In contrast to what is stated in the manual, the confidence interval size - `conf_sig` was 0.6 by default instead of 0.9. + + In contrast to what is stated in the manual, the confidence interval size + `conf_sig` was 0.6 by default instead of 0.9. - + Forecasting with exogenous deterministic variables provided wrong decision - rules, yielding wrong forecasts. + + Forecasting with exogenous deterministic variables provided wrong decision + rules, yielding wrong forecasts. - + Forecasting with exogenous deterministic variables crashed when the - `periods` option was not explicitly specified, + + Forecasting with exogenous deterministic variables crashed when the + `periods` option was not explicitly specified, - + Option `forecast` when used with `initval` was using the initial values in - the `initval` block and not the steady state computed from these initial - values as the starting point of forecasts. + + Option `forecast` when used with `initval` was using the initial values in + the `initval` block and not the steady state computed from these initial + values as the starting point of forecasts. - Global Sensitivity Analysis - + Sensitivity with ML estimation could result in crashes, + + Sensitivity with ML estimation could result in crashes, - + Option `mc` must be forced if `neighborhood_width` is used, + + Option `mc` must be forced if `neighborhood_width` is used, - + Fixed dimension of `stock_logpo` and `stock_ys`, + + Fixed dimension of `stock_logpo` and `stock_ys`, - + Incomplete variable initialization could lead to crashes with `prior_range=1`. + + Incomplete variable initialization could lead to crashes with `prior_range=1`. - Indentification - + Identification did not correctly pass the `lik_init` option, - requiring the manual setting of `options_.diffuse_filter=1` in - case of unit roots, + + Identification did not correctly pass the `lik_init` option, + requiring the manual setting of `options_.diffuse_filter=1` in + case of unit roots, - + Testing identification of standard deviations as the only - parameters to be estimated with ML leaded to crashes, + + Testing identification of standard deviations as the only + parameters to be estimated with ML leaded to crashes, - + Automatic increase of the lag number for autocovariances when the - number of parameters is bigger than the number of non-zero moments - was broken + + Automatic increase of the lag number for autocovariances when the + number of parameters is bigger than the number of non-zero moments + was broken, - + When using ML, the asymptotic Hessian was not computed, + + When using ML, the asymptotic Hessian was not computed, - + Checking for singular values when the eigenvectors contained only - one column did not work correctly, + + Checking for singular values when the eigenvectors contained only + one column did not work correctly, - Model comparison - + Selection of the `modifiedharmonicmean` estimator was broken, + + Selection of the `modifiedharmonicmean` estimator was broken, - Optimal Simple Rules - + When covariances were specified, variables that only entered with - their variance and no covariance term obtained a wrong weight, - resulting in wrong results, + + When covariances were specified, variables that only entered with + their variance and no covariance term obtained a wrong weight, + resulting in wrong results, - + Results reported for stochastic simulations after `osr` were based - on the last parameter vector encountered during optimization, - which does not necessarily coincide with the optimal parameter - vector, + + Results reported for stochastic simulations after `osr` were based + on the last parameter vector encountered during optimization, + which does not necessarily coincide with the optimal parameter + vector, - + Using only one (co)variance in the objective function resulted in crashes, + + Using only one (co)variance in the objective function resulted in crashes, - + For models with non-stationary variables the objective function was computed wrongly. + + For models with non-stationary variables the objective function was computed wrongly. - Ramsey policy - + If a Lagrange multiplier appeared in the model with a lead or a lag - of more than one period, the steady state could be wrong. + + If a Lagrange multiplier appeared in the model with a lead or a lag + of more than one period, the steady state could be wrong. - + When using an external steady state file, incorrect steady states - could be accepted, + + When using an external steady state file, incorrect steady states + could be accepted, - + When using an external steady state file with more than one - instrument, Dynare crashed, + + When using an external steady state file with more than one + instrument, Dynare crashed, - + When using an external steady state file and running `stoch_simul` - after `ramsey_planner`, an incorrect steady state was used, + + When using an external steady state file and running `stoch_simul` + after `ramsey_planner`, an incorrect steady state was used, - + When the number of instruments was not equal to the number of - omitted equations, Dynare crashed with a cryptic message, + + When the number of instruments was not equal to the number of + omitted equations, Dynare crashed with a cryptic message, - + The `planner_objective` accepted `varexo`, but ignored them for computations, + + The `planner_objective` accepted `varexo`, but ignored them for computations, - Shock decomposition - + Did not work with the `parameter_set=calibration` option if an - `estimated_params` block is present, + + Did not work with the `parameter_set=calibration` option if an + `estimated_params` block is present, - + Crashed after MLE. + + Crashed after MLE. - Perfect foresight models - + The perfect foresight solver could accept a complex solution - instead of continuing to look for a real-valued one, + + The perfect foresight solver could accept a complex solution + instead of continuing to look for a real-valued one, - + The `initval_file` command only accepted column and not row vectors, + + The `initval_file` command only accepted column and not row vectors, - + The `initval_file` command did not work with Excel files, + + The `initval_file` command did not work with Excel files, - + Deterministic simulations with one boundary condition crashed in - `solve_one_boundary` due to a missing underscore when passing - `options_.simul.maxit`, + + Deterministic simulations with one boundary condition crashed in + `solve_one_boundary` due to a missing underscore when passing + `options_.simul.maxit`, - + Deterministic simulation with exogenous variables lagged by more - than one period crashed, + + Deterministic simulation with exogenous variables lagged by more + than one period crashed, - + Termination criterion `maxit` was hard-coded for `solve_algo=0` - and could no be changed, + + Termination criterion `maxit` was hard-coded for `solve_algo=0` + and could no be changed, - + When using `block`/`bytecode`, relational operators could not be enforced, + + When using `block`/`bytecode`, relational operators could not be enforced, - + When using `block` some exceptions were not properly handled, - leading to code crashes, + + When using `block` some exceptions were not properly handled, + leading to code crashes, - + Using `periods=1` crashed the solver (bug only partially fixed). + + Using `periods=1` crashed the solver (bug only partially fixed). - Smoothing - + The univariate Kalman smoother returned wrong results when used - with correlated measurement error, + + The univariate Kalman smoother returned wrong results when used + with correlated measurement error, - + The diffuse smoother sometimes returned linear combinations of the - smoothed stochastic trend estimates instead of the original trend - estimates. + + The diffuse smoother sometimes returned linear combinations of the + smoothed stochastic trend estimates instead of the original trend + estimates. - Perturbation reduced form - + In contrast to what is stated in the manual, the results of the - unconditional variance decomposition were only stored in - `oo_.gamma_y(nar+2)`, not in `oo_.variance_decomposition`, + + In contrast to what is stated in the manual, the results of the + unconditional variance decomposition were only stored in + `oo_.gamma_y(nar+2)`, not in `oo_.variance_decomposition`, - + Dynare could crash when the steady state could not be computed - when using the `loglinear` option, + + Dynare could crash when the steady state could not be computed + when using the `loglinear` option, - + Using `bytcode` when declared exogenous variables were not - used in the model leaded to crashes in stochastic simulations, + + Using `bytcode` when declared exogenous variables were not + used in the model leaded to crashes in stochastic simulations, - + Displaying decision rules involving lags of auxiliary variables of - type 0 (leads>1) crashed. + + Displaying decision rules involving lags of auxiliary variables of + type 0 (leads>1) crashed. - + The `relative_irf` option resulted in wrong output at `order>1` as - it implicitly relies on linearity. + + The `relative_irf` option resulted in wrong output at `order>1` as + it implicitly relies on linearity. - Displaying of the MH-history with the `internals` command crashed @@ -787,6 +794,7 @@ Here is the list of major user-visible changes: variables and parameters. + Announcement for Dynare 4.4.3 (on 2014-07-31) =============================================