Bayesian GMM estimator

This is a repo for the C++ program that implements the Bayesian GMM estimator of Gallant, Giacomini and Ragusa (2017). The program heavily relies on Gallant's MLE package.

Directory Structure

• base_model: this folder contains the heart of the MCMC estimator. In principle, this part should be independent from the specific project.

  • initialize: reads the InputParamFile (example) and defines the specification class
    • source files: main.cpp, initialize.cpp
    • header files: initialize.h
  • estimator: elements of the mcmc sampler/optimizer, it generates the mcmc class
    • source files: asymptotics.cpp, mcmc_class.cpp, proposal.cpp
    • header files: estimator_base.h, estimator.h
  • libscl: slightly altered version of Gallant's statistical library (including the gmm class)

• xxx.example: these foders belong to separate projects (indicated by the prefix xxx). In addition to the subfolders detailed below, they contain (1) the makefile that generates the executable bayes_gmm, (2) the InputParamFile detailing the specifics of the estimator (3) a python script generating summary statistics and plots from the result files. Each project directory must contain three subfolders:

  • data: this contains the data (in the file named data.dat; variables in columns separated by whitespaces) and the initial_particle.dat file containing an intial draw of particles for the conditional particle filter.
  • usermodel: defines the usermodel class
    • source files: usermodel.cpp, moments.cpp, model.cpp, default_params.cpp
    • header files: usermodel.h, moments.h, model.h, default_params.h
  • result_files: a plethora of .dat files generated by the estimator for diagnoses and further analyses. It has a subfolder with figures and summary tables generated by plot_generator.py

How to run the examples