v0.1.7

MacroModelling v0.1.7

Diff since v0.1.6

• improve docs
• fix steady state finder
• move models to specific folder

v0.1.6

MacroModelling v0.1.6

Diff since v0.1.5

• add conditional forecasting (#14) (@thorek1)
• include two models: GNSS (2010) and Ascar and Sbordone (2014)
• add functions to translate to and from dynare mod files (experimental) (#13) (@thorek1)
• base solution on symbolic model derivatives up to third order (#15) (@thorek1)
• make kalman filter compatible with both reverse and forward mode AD (#15) (@thorek1)

v0.1.5

MacroModelling v0.1.5

Diff since v0.1.4

Merged pull requests:

• Shock history input for irf/plots (#12) (@thorek1)

v0.1.4

MacroModelling v0.1.4

Diff since v0.1.3

• added conditional variance decomposition incl. plot
• added plots of the solution (transition & policy function)

v0.1.3

MacroModelling v0.1.3

Diff since v0.1.2

• add correlation, autocorrelation, and variance decomposition
• add convenience functions: get_variance, get_standard_deviation, get_covariance, get_std, get_var, get_covar, std, var, covar, get_corr, corr, get_autocorr, autocorr
• improve docs
• speed up covariance calculation
• allow for conditional parameters at end of calibration equation as well (... | alpha instead of alpha | ...)

v0.1.2

MacroModelling v0.1.2

Diff since v0.1.1

• fix documentation

v0.1.1

MacroModelling v0.1.1

Diff since v0.1.0

• estimation tutorial
• SS solver improvements
• more models
• extensive functionality testing

v0.1.0 first release

MacroModelling.jl - fast prototyping of dynamic stochastic general equilibrium (DSGE) models

MacroModelling.jl currently supports dicsrete-time DSGE models and the timing of a variable reflects when the variable is decided (end of period for stock variables).

As of now MacroModelling.jl can:

• parse a model written with user friendly syntax (variables are followed by time indices ...[2], [1], [0], [-1], [-2]..., or [x] for shocks)
• (tries to) solve the model only knowing the model equations and parameter values (no steady state file needed)
• calculate first, second, and third order perturbation solutions using (forward) automatic differentiation (AD)
• calculate (generalised) impulse response functions, and simulate the model
• calibrate parameters using (non stochastic) steady state relationships
• match model moments
• estimate the model on data (kalman filter using first order perturbation)
• differentiate (forward AD) the model solution (first order perturbation), kalman filter loglikelihood, model moments, steady state, with respect to the parameters

MacroModelling.jl helps the modeller:

• Syntax makes variable and parameter definitions obsolete
• MacroModelling.jl applies symbolic and numerical tools to solve for the steady state (and mostly succeeds without much help)