Next: Displaying and saving results, Previous: Sensitivity and identification analysis, Up: The Model file [Contents][Index]

### 4.21 Markov-switching SBVAR

Given a list of variables, observed variables and a data file, Dynare can be used to solve a Markov-switching SBVAR model according to Sims, Waggoner and Zha (2008).10 Having done this, you can create forecasts and compute the marginal data density, regime probabilities, IRFs, and variance decomposition of the model.

The commands have been modularized, allowing for multiple calls to the same command within a `<mod_file>.mod` file. The default is to use `<mod_file>` to tag the input (output) files used (produced) by the program. Thus, to call any command more than once within a `<mod_file>.mod` file, you must use the `*_tag` options described below.

Command: markov_switching (OPTIONS…);

  Description

  Declares the Markov state variable information of a Markov-switching SBVAR model.

  Options

  `chain = INTEGER`

    The Markov chain considered. Default: `none`

  `number_of_regimes = INTEGER`

    Specifies the total number of regimes in the Markov Chain. This is a required option.

  `duration = DOUBLE | [ROW VECTOR OF DOUBLES]`

    The duration of the regimes or regimes. This is a required option. When passed a scalar real number, it specifies the average duration for all regimes in this chain. When passed a vector of size equal `number_of_regimes`, it specifies the average duration of the associated regimes `(1:number_of_regimes)` in this chain. An absorbing state can be specified through the restrictions-option.

  `restrictions = [[ROW VECTOR OF 3 DOUBLES],[ROW VECTOR OF 3 DOUBLES],...]`

    Provides restrictions on this chain’s regime transition matrix. Its vector argument takes three inputs of the form: `[current_period_regime, next_period_regime, transition_probability]`

    The first two entries are positive integers, and the third is a non-negative real in the set [0,1]. If restrictions are specified for every transition for a regime, the sum of the probabilities must be 1. Otherwise, if restrictions are not provided for every transition for a given regime the sum of the provided transition probabilities msut be <1. Regardless of the number of lags, the restrictions are specified for parameters at time `t` since the transition probability for a parameter at `t` is equal to that of the parameter at `t-1`.

  In case of estimating a MS-DSGE model,11 in addition the following options are allowed:

  `parameters = [LIST OF PARAMETERS]`

    This option specifies which parameters are controlled by this Markov Chain.

  `number_of_lags = DOUBLE`

    Provides the number of lags that each parameter can take within each regime in this chain.

  Example

      markov_switching(chain=1, duration=2.5, restrictions=[[1,3,0],[3,1,0]]);

  Specifies a Markov-switching BVAR with a first chain with 3 regimes that all have a duration of 2.5 periods. The probability of directly going from regime 1 to regime 3 and vice versa is 0.

  Example

      markov_switching(chain=2, number_of_regimes=3, duration=[0.5, 2.5, 2.5],
      parameter=[alpha, rho], number_of_lags=2, restrictions=[[1,3,0],[3,3,1]]);

  Specifies a Markov-switching DSGE model with a second chain with 3 regimes that have durations of 0.5, 2.5, and 2.5 periods, respectively. The switching parameters are `alpha` and `rho`. The probability of directly going from regime 1 to regime 3 is 0, while regime 3 is an absorbing state.

Command: svar (OPTIONS…);

  Description

  Each Markov chain can control the switching of a set of parameters. We allow the parameters to be divided equation by equation and by variance or slope and intercept.

  Options

  `coefficients`

    Specifies that only the slope and intercept in the given equations are controlled by the given chain. One, but not both, of `coefficients` or `variances` must appear. Default: `none`

  `variances`

    Specifies that only variances in the given equations are controlled by the given chain. One, but not both, of `coefficients` or `variances` must appear. Default: `none`

  `equations`

    Defines the equation controlled by the given chain. If not specified, then all equations are controlled by `chain`. Default: `none`

  `chain = INTEGER`

    Specifies a Markov chain defined by markov_switching. Default: `none`

Command: sbvar (OPTIONS…);

  Description

  To be documented. For now, see the wiki: http://www.dynare.org/DynareWiki/SbvarOptions

  Options

  `datafile`
  `freq`
  `initial_year`
  `initial_subperiod`
  `final_year`
  `final_subperiod`
  `data`
  `vlist`
  `vlistlog`
  `vlistper`
  `restriction_fname`
  `nlags`
  `cross_restrictions`
  `contemp_reduced_form`
  `real_pseudo_forecast`
  `no_bayesian_prior`
  `dummy_obs`
  `nstates`
  `indxscalesstates`
  `alpha`
  `beta`
  `gsig2_lmdm`
  `q_diag`
  `flat_prior`
  `ncsk`
  `nstd`
  `ninv`
  `indxparr`
  `indxovr`
  `aband`
  `indxap`
  `apband`
  `indximf`
  `indxfore`
  `foreband`
  `indxgforhat`
  `indxgimfhat`
  `indxestima`
  `indxgdls`
  `eq_ms`
  `cms`
  `ncms`
  `eq_cms`
  `tlindx`
  `tlnumber`
  `cnum`
  `forecast`
  `coefficients_prior_hyperparameters`

Block: svar_identification ;

  Description

  This block is terminated by `end;`, and contains lines of the form:

      UPPER_CHOLESKY;
      LOWER_CHOLESKY;
      EXCLUSION CONSTANTS;
      EXCLUSION LAG INTEGER; VARIABLE_NAME [,VARIABLE_NAME…]
      EXCLUSION LAG INTEGER; EQUATION INTEGER, VARIABLE_NAME [,VARIABLE_NAME…]
      RESTRICTION EQUATION INTEGER, EXPRESSION = EXPRESSION;

  To be documented. For now, see the wiki: http://www.dynare.org/DynareWiki/MarkovSwitchingInterface

Command: ms_estimation (OPTIONS…);

  Description

  Triggers the creation of an initialization file for, and the estimation of, a Markov-switching SBVAR model. At the end of the run, the , , and matrices are contained in the `oo_.ms` structure.

  Options

  General Options

  `file_tag = FILENAME`

    The portion of the filename associated with this run. This will create the model initialization file, `init_<file_tag>.dat`. Default: `<mod_file>`

  `output_file_tag = FILENAME`

    The portion of the output filename that will be assigned to this run. This will create, among other files, `est_final_<output_file_tag>.out`, `est_intermediate_<output_file_tag>.out`. Default: `<file_tag>`

  `no_create_init`

    Do not create an initialization file for the model. Passing this option will cause the Initialization Options to be ignored. Further, the model will be generated from the output files associated with the previous estimation run (i.e. `est_final_<file_tag>.out`, `est_intermediate_<file_tag>.out` or `init_<file_tag>.dat`, searched for in sequential order). This functionality can be useful for continuing a previous estimation run to ensure convergence was reached or for reusing an initialization file. NB: If this option is not passed, the files from the previous estimation run will be overwritten. Default: `off` (i.e. create initialization file)

  Initialization Options

  `coefficients_prior_hyperparameters = [DOUBLE1 DOUBLE2 DOUBLE3 DOUBLE4 DOUBLE5 DOUBLE6]`

    Sets the hyper parameters for the model. The six elements of the argument vector have the following interpretations:

    `Position`

      `Interpretation`

    `1`

      Overall tightness for and

    `2`

      Relative tightness for

    `3`

      Relative tightness for the constant term

    `4`

      Tightness on lag decay (range: 1.2 - 1.5); a faster decay produces better inflation process

    `5`

      Weight on nvar sums of coeffs dummy observations (unit roots)

    `6`

      Weight on single dummy initial observation including constant

    Default: `[1.0 1.0 0.1 1.2 1.0 1.0]`

  `freq = INTEGER | `monthly` | `quarterly` | `yearly``

    Frequency of the data (e.g. `monthly`, `12`). Default: `4`

  `initial_year = INTEGER`

    The first year of data. Default: `none`

  `initial_subperiod = INTEGER`

    The first period of data (i.e. for quarterly data, an integer in [`1,4`]). Default: `1`

  `final_year = INTEGER`

    The last year of data. Default: Set to encompass entire dataset.

  `final_subperiod = INTEGER`

    The final period of data (i.e. for monthly data, an integer in [`1,12`]. Default: When final_year is also missing, set to encompass entire dataset; when final_year is indicated, set to the maximum number of subperiods given the frequency (i.e. 4 for quarterly data, 12 for monthly,...).

  `datafile = FILENAME`

    See datafile.

  `xls_sheet = NAME`

    See xls_sheet.

  `xls_range = RANGE`

    See xls_range.

  `nlags = INTEGER`

    The number of lags in the model. Default: `1`

  `cross_restrictions`

    Use cross and restrictions. Default: `off`

  `contemp_reduced_form`

    Use contemporaneous recursive reduced form. Default: `off`

  `no_bayesian_prior`

    Do not use Bayesian prior. Default: `off` (i.e. use Bayesian prior)

  `alpha = INTEGER`

    Alpha value for squared time-varying structural shock lambda. Default: `1`

  `beta = INTEGER`

    Beta value for squared time-varying structural shock lambda. Default: `1`

  `gsig2_lmdm = INTEGER`

    The variance for each independent parameter under `SimsZha` restrictions. Default: `50^2`

  `specification = `sims_zha` | `none``

    This controls how restrictions are imposed to reduce the number of parameters. Default: `Random Walk`

  Estimation Options

  `convergence_starting_value = DOUBLE`

    This is the tolerance criterion for convergence and refers to changes in the objective function value. It should be rather loose since it will gradually be tightened during estimation. Default: `1e-3`

  `convergence_ending_value = DOUBLE`

    The convergence criterion ending value. Values much smaller than square root machine epsilon are probably overkill. Default: `1e-6`

  `convergence_increment_value = DOUBLE`

    Determines how quickly the convergence criterion moves from the starting value to the ending value. Default: `0.1`

  `max_iterations_starting_value = INTEGER`

    This is the maximum number of iterations allowed in the hill-climbing optimization routine and should be rather small since it will gradually be increased during estimation. Default: `50`

  `max_iterations_increment_value = DOUBLE`

    Determines how quickly the maximum number of iterations is increased. Default: `2`

  `max_block_iterations = INTEGER`

    The parameters are divided into blocks and optimization proceeds over each block. After a set of blockwise optimizations are performed, the convergence criterion is checked and the blockwise optimizations are repeated if the criterion is violated. This controls the maximum number of times the blockwise optimization can be performed. Note that after the blockwise optimizations have converged, a single optimization over all the parameters is performed before updating the convergence value and maximum number of iterations. Default: `100`

  `max_repeated_optimization_runs = INTEGER`

    The entire process described by max_block_iterations is repeated until improvement has stopped. This is the maximum number of times the process is allowed to repeat. Set this to `0` to not allow repetitions. Default: `10`

  `function_convergence_criterion = DOUBLE`

    The convergence criterion for the objective function when `max_repeated_optimizations_runs` is positive. Default: `0.1`

  `parameter_convergence_criterion = DOUBLE`

    The convergence criterion for parameter values when `max_repeated_optimizations_runs` is positive. Default: `0.1`

  `number_of_large_perturbations = INTEGER`

    The entire process described by max_block_iterations is repeated with random starting values drawn from the posterior. This specifies the number of random starting values used. Set this to `0` to not use random starting values. A larger number should be specified to ensure that the entire parameter space has been covered. Default: `5`

  `number_of_small_perturbations = INTEGER`

    The number of small perturbations to make after the large perturbations have stopped improving. Setting this number much above `10` is probably overkill. Default: `5`

  `number_of_posterior_draws_after_perturbation = INTEGER`

    The number of consecutive posterior draws to make when producing a small perturbation. Because the posterior draws are serially correlated, a small number will result in a small perturbation. Default: `1`

  `max_number_of_stages = INTEGER`

    The small and large perturbation are repeated until improvement has stopped. This specifics the maximum number of stages allowed. Default: `20`

  `random_function_convergence_criterion = DOUBLE`

    The convergence criterion for the objective function when `number_of_large_perturbations` is positive. Default: `0.1`

  `random_parameter_convergence_criterion = DOUBLE`

    The convergence criterion for parameter values when `number_of_large_perturbations` is positive. Default: `0.1`

Example

    ms_estimation(datafile=data, initial_year=1959, final_year=2005,
    nlags=4, max_repeated_optimization_runs=1, max_number_of_stages=0);

    ms_estimation(file_tag=second_run, datafile=data, initial_year=1959,
    final_year=2005, nlags=4, max_repeated_optimization_runs=1,
    max_number_of_stages=0);

    ms_estimation(file_tag=second_run, output_file_tag=third_run,
    no_create_init, max_repeated_optimization_runs=5,
    number_of_large_perturbations=10);

Command: ms_simulation ;
Command: ms_simulation (OPTIONS…);

  Description

  Simulates a Markov-switching SBVAR model.

  Options

  `file_tag = FILENAME`

    The portion of the filename associated with the `ms_estimation` run. Default: `<mod_file>`

  `output_file_tag = FILENAME`

    The portion of the output filename that will be assigned to this run. Default: `<file_tag>`

  `mh_replic = INTEGER`

    The number of draws to save. Default: `10,000`

  `drop = INTEGER`

    The number of burn-in draws. Default: `0.1*mh_replic*thinning_factor`

  `thinning_factor = INTEGER`

    The total number of draws is equal to `thinning_factor*mh_replic+drop`. Default: `1`

  `adaptive_mh_draws = INTEGER`

    Tuning period for Metropolis-Hastings draws. Default: `30,000`

  `save_draws`

    Save all elements of , , , and , to a file named `draws_<<file_tag>>.out` with each draw on a separate line. A file that describes how these matrices are laid out is contained in `draws_header_<<file_tag>>.out`. A file called `load_flat_file.m` is provided to simplify loading the saved files into the corresponding variables `A0`, `Aplus`, `Q`, and `Zeta` in your MATLAB/Octave workspace. Default: `off`

Example

    ms_simulation(file_tag=second_run);

    ms_simulation(file_tag=third_run, mh_replic=5000, thinning_factor=3);

Command: ms_compute_mdd ;
Command: ms_compute_mdd (OPTIONS…);

  Description

  Computes the marginal data density of a Markov-switching SBVAR model from the posterior draws. At the end of the run, the Muller and Bridged log marginal densities are contained in the `oo_.ms` structure.

  Options

  `file_tag = FILENAME`

    See file_tag.

  `output_file_tag = FILENAME`

    See output_file_tag.

  `simulation_file_tag = FILENAME`

    The portion of the filename associated with the simulation run. Default: `<file_tag>`

  `proposal_type = INTEGER`

    The proposal type:

    `1`

      Gaussian

    `2`

      Power

    `3`

      Truncated Power

    `4`

      Step

    `5`

      Truncated Gaussian

    Default: `3`

  `proposal_lower_bound = DOUBLE`

    The lower cutoff in terms of probability. Not used for `proposal_type` in [`1,2`]. Required for all other proposal types. Default: `0.1`

  `proposal_upper_bound = DOUBLE`

    The upper cutoff in terms of probability. Not used for `proposal_type` equal to `1`. Required for all other proposal types. Default: `0.9`

  `mdd_proposal_draws = INTEGER`

    The number of proposal draws. Default: `100,000`

  `mdd_use_mean_center`

    Use the posterior mean as center. Default: `off`

Command: ms_compute_probabilities ;
Command: ms_compute_probabilities (OPTIONS…);

  Description

  Computes smoothed regime probabilities of a Markov-switching SBVAR model. Output `.eps` files are contained in `<output_file_tag/Output/Probabilities>`.

  Options

  `file_tag = FILENAME`

    See file_tag.

  `output_file_tag = FILENAME`

    See output_file_tag.

  `filtered_probabilities`

    Filtered probabilities are computed instead of smoothed. Default: `off`

  `real_time_smoothed`

    Smoothed probabilities are computed based on time `t` information for . Default: `off`

Command: ms_irf ;
Command: ms_irf (OPTIONS…);

  Description

  Computes impulse response functions for a Markov-switching SBVAR model. Output `.eps` files are contained in `<output_file_tag/Output/IRF>`, while data files are contained in `<output_file_tag/IRF>`.

  Options

  `file_tag = FILENAME`

    See file_tag.

  `output_file_tag = FILENAME`

    See output_file_tag.

  `simulation_file_tag = FILENAME`

    See simulation_file_tag.

  `horizon = INTEGER`

    The forecast horizon. Default: `12`

  `filtered_probabilities`

    Uses filtered probabilities at the end of the sample as initial conditions for regime probabilities. Only one of `filtered_probabilities`, `regime` and `regimes` may be passed. Default: `off`

  `error_band_percentiles = [DOUBLE1 …]`

    The percentiles to compute. Default: `[0.16 0.50 0.84]`. If `median` is passed, the default is `[0.5]`

  `shock_draws = INTEGER`

    The number of regime paths to draw. Default: `10,000`

  `shocks_per_parameter = INTEGER`

    The number of regime paths to draw under parameter uncertainty. Default: `10`

  `thinning_factor = INTEGER`

    Only of the draws in posterior draws file are used. Default: `1`

  `free_parameters = NUMERICAL_VECTOR`

    A vector of free parameters to initialize theta of the model. Default: use estimated parameters

  `parameter_uncertainty`

    Calculate IRFs under parameter uncertainty. Requires that `ms_simulation` has been run. Default: `off`

  `regime = INTEGER`

    Given the data and model parameters, what is the ergodic probability of being in the specified regime. Only one of `filtered_probabilities`, `regime` and `regimes` may be passed. Default: `off`

  `regimes`

    Describes the evolution of regimes. Only one of `filtered_probabilities`, `regime` and `regimes` may be passed. Default: `off`

  `median`

    A shortcut to setting `error_band_percentiles=[0.5]`. Default: `off`

Command: ms_forecast ;
Command: ms_forecast (OPTIONS…);

  Description

  Generates forecasts for a Markov-switching SBVAR model. Output `.eps` files are contained in `<output_file_tag/Output/Forecast>`, while data files are contained in `<output_file_tag/Forecast>`.

  Options

  `file_tag = FILENAME`

    See file_tag.

  `output_file_tag = FILENAME`

    See output_file_tag.

  `simulation_file_tag = FILENAME`

    See simulation_file_tag.

  `data_obs_nbr = INTEGER`

    The number of data points included in the output. Default: `0`

  `error_band_percentiles = [DOUBLE1 …]`

    See error_band_percentiles.

  `shock_draws = INTEGER`

    See shock_draws.

  `shocks_per_parameter = INTEGER`

    See shocks_per_parameter.

  `thinning_factor = INTEGER`

    See thinning_factor.

  `free_parameters = NUMERICAL_VECTOR`

    See free_parameters.

  `parameter_uncertainty`

    See parameter_uncertainty.

  `regime = INTEGER`

    See regime.

  `regimes`

    See regimes.

  `median`

    See median.

  `horizon = INTEGER`

    See horizon.

Command: ms_variance_decomposition ;
Command: ms_variance_decomposition (OPTIONS…);

  Description

  Computes the variance decomposition for a Markov-switching SBVAR model. Output `.eps` files are contained in `<output_file_tag/Output/Variance_Decomposition>`, while data files are contained in `<output_file_tag/Variance_Decomposition>`.

  Options

  `file_tag = FILENAME`

    See file_tag.

  `output_file_tag = FILENAME`

    See output_file_tag.

  `simulation_file_tag = FILENAME`

    See simulation_file_tag.

  `horizon = INTEGER`

    See horizon.

  `filtered_probabilities`

    See filtered_probabilities.

  `no_error_bands`

    Do not output percentile error bands (i.e. compute mean). Default: `off` (i.e. output error bands)

  `error_band_percentiles = [DOUBLE1 …]`

    See error_band_percentiles.

  `shock_draws = INTEGER`

    See shock_draws.

  `shocks_per_parameter = INTEGER`

    See shocks_per_parameter.

  `thinning_factor = INTEGER`

    See thinning_factor.

  `free_parameters = NUMERICAL_VECTOR`

    See free_parameters.

  `parameter_uncertainty`

    See parameter_uncertainty.

  `regime = INTEGER`

    See regime.

  `regimes`

    See regimes.

#### Footnotes

### (10)

If you want to align the paper with the description herein, please note that is and is .

### (11)

An example can be found at https://github.com/DynareTeam/dynare/blob/master/tests/ms-dsge/test_ms_dsge.mod.

Next: Displaying and saving results, Previous: Sensitivity and identification analysis, Up: The Model file [Contents][Index]