v0.4.0

New features

• Added nelder_mead optimizer
• Added forecasting under alternative policies (AltPolicy) and alternative
  scenarios (AbstractScenario)
• Added plotting functions: plot_parameters, plot_history_and_forecast,
  plot_forecast_comparison, hair_plot, plot_shock_decomposition,
  plot_impulse_response, plot_altpolicies, and plot_scenario

Breaking changes

• Upgraded all code for use with Julia v0.6.0 or higher
• Changed input data file names: see get_data_filename
  • Added dataset identifier Setting with key data_id
  • Changed cond_id from Setting{String} to Setting{Int}
  • Moved raw input data files from inpath(m, "data") to inpath(m, "raw")
• Added :marginal_L (marginal likelihood) field to Kalman type
• Removed MM and VVall fields from Measurement type
• Pluralized forecast output classes :states, :shocks, and :stdshocks
• Stopped adding back population growth when reverse transforming shock
  decompositions and deterministic trends
• Stopped adding trends to and detrending shock decompositions and deterministic
  trends
• Changed pseudo-observable implementation to correspond one-to-one with
  observables
  • Changed PseudoObservableMapping type (and field in System type) to
    PseudoMeasurement
  • Added m.pseudo_observables and m.pseudo_observable_mappings fields to
    AbstractModel subtypes
  • Pseudo-observable-related things are no longer Nullable. Instead, if no
    pseudo-measurement equation is implemented, the fields in the model object
    are empty dictionaries
• Refactored means and bands computation
  • Renamed means_bands_all to compute_meansbands
  • Renamed meansbands_matrix_all to meansbands_to_matrix

v0.3.1

Bug fixes

• Added the following subspecs:
  • Model 990, subspec 3: fixes bugs 1-4 in
    FRBNY-DSGE/DSGE-2015-Apr#1
  • Model 1002, subspec 10: corrects the definition of betabar to use
    m[:σ_c] instead of σ_ω_star
  • Model 1010, subspec 20: similarly corrects the definition of betabar

Deprecation Fixes

• Implemented transpose for Parameters so that matrix division (i.e. the
  (\) operator) no longer throws a warning

v0.3.0

New features

• detexify function turns unicode characters into ASCII strings
  before writing them to CSV.

Breaking changes

• Changed Dicts of indices in model object to OrderedDicts
• Upgrade all code for use with Julia v0.5.1 or higher

v0.2.0

New features

• Added the An-Schorfheide model, a simple three-equation New Keynesian model.
• Added Model 1010, an updated version of Model 1002.
• Added three optimization methods: :simulated_annealing, :LBFGS, and
  :combined_optimizer (which alternates between simulated annealing and
  LBFGS).
• Added the PseudoObservable type and the pseudo_measurement function, which
  defines pseudo-observables (linear combinations of states which are not
  observed) for each model, e.g. the output gap.
• Implemented the forecast step, a suite of functions that forecast using
  estimated parameters and compute means and bands of the forecasted series. The
  top-level functions are forecast_one and means_bands_all; see the
  forecasting and
  means and bands
  for more details.

Breaking changes

• Added the Observable type; replaced the data_series and data_transforms
  fields in the model type definitions with
  observable_mappings::OrderedDict{Symbol, Observable}, which is initialized
  in init_observable_mappings!.
• kalman_filter has been broken out into
  StateSpaceRoutines.jl.
• estimate now saves only parameter draws, not the associated state-space
  matrices or the last filtered states for each draw.

v0.1.5

New Features

• Added Model 1002, an updated version of Model 990.
• Added documentation for Model 1002 at docs/DSGE_Model_Documentation_1002.pdf.
  This pdf includes an overview of the economic theory underlying the model, a
  summary of the model's main equations, a description of
  the data used, a table of priors for the model's parameters,
  and more.

Deprecation Fixes

• Optim.jl's MultivariateOptimizationResults type requires f_increased field
• MersenneTwister must be constructed with a seed

SMC Replication

This release of the FRBNY DSGE.jl package implements Sequential Monte Carlo (SMC) sampling as an alternative to Metropolis Hastings Markov Chain Monte Carlo sampling. The SMC algorithm implemented here is based upon Edward Herbst and Frank Schorfheide's paper "Sequential Monte Carlo Sampling for DSGE Models" and the code accompanying their book Bayesian Estimation of DSGE Models. More information and the original MATLAB scripts that this code replicates can be found at Frank Schorfheide's website. Currently, FRBNY's implementation of SMC works on the small-scale New Keynesian DSGE model presented in Sungbae An and Frank Schorfheide's paper "Bayesian Analysis of DSGE Models". FRBNY is currently working on extending the code so that SMC may be used with medium-scale DSGE models. This and other extensions of the DSGE model code may be released in the future at the discretion of FRBNY. Comments and suggestions are welcome, and best submitted as either an issue or a pull request to this branch.

v0.1.4

New Features

• Gensys no longer throws an error when system is indeterminate;
  instead, a warning is printed to the screen.

Bug Fixes

• Fix OptimizationTrace constructor according to Optim v0.6. See #6.

v0.1.3

New features

• Automatic dataset download and generation
• More robust and flexible treatment of dataset- and model-related dates
• Refactored computational settings
• Improved infrastructure for organizing input/output files
• Bug fix in treatment of zero lower bound in posterior computation
• Improved test coverage and documentation

Breaking changes

• Input data matrices are CSV instead of HDF5
• Estimation output matrices are not flattened when saved