OriginalThoughts

Original thoughts about the DynareJulia project

strengths and weaknesses of Dynare

Strong points

• A simple and intuitive modeling language used in many papers and projects (see Macroeconomic Model Data Base, model collections such as Johannes Pfeifer at (https://github.com/JohannesPfeifer/DSGE_mod) and many more)
• Help for replication
• A very efficient preprocessor (including symbolic derivation)
• Fast algorithms
• A unique environment for several connected tasks:
  • Solution of the model
  • Simulation
  • Estimation (Bayesian, ML, methods of moments)
  • Forecasting
  • Optimal policy
  • Occasionally binding constraints
  • Markov-Switching VAR
  • Simple time series operations
  • Reporting results
• A lively community (forum, Summer school, Dynare conferences)
• Fast correction of bugs
• Has been adopted for teaching

Some birth defects

• Too much concern about memory footprint
  • selecting variables present with leads or lags,
  • in perfect foresight algorithm: triangularizing the Jacobian matrix of the stacked system period by period, instead of using sparse matrix code (corrected since).
  • partly due to hardware limitations in mid-1990
• Ignoring time series nature of data (partly corrected with dseries)
• Absence of block for calibrating parameters
• Absence of a documented API for the main numerical functions.
• Difficulties in handling contributions

New directions

• Estimation of models with zero lower bound
• Support for heterogeneous agents models
• Global solution

Algorithms

Discussion of some of the algorithms used for solving the model and estimating its parameters can be found here

DYNARE-JULIA

Rewriting Dynare

• More than 20 years of development layers
• Two language problem: fast code must be written in C, C++ or Fortran
• Matlab doesn't have an ecosystem or a community of open source developers
• Matlab price tag is a deterrent for many users and Octave is too slow

Why Julia

• meant for scientific computing
• JIT compiling
• solves the two language problem
• user defined types, no classes, multiple dispatch
• rich collection of existing packages
• lively community
• efficient package management
• powerful metaprogramming

Dynare-Julia

• Different packages for numerical algorithms and for interpretation of Dynare modeling language
• Re-using Dynare C++ preprocessor with special output for Julia
• Insist on modularity
• Homogeneous coding style
• Leveraging existing Julia packages (NLsolve, Plots, PrettyTables)