Bo E. Honoré, Thomas H. Jørgensen, and Áureo de Paula, "The 
Informativeness of Estimation Moments", Journal of Applied Econometrics, 
Vol. 37, No. 7, 2020, pp. 797-813.

The data used in this paper cannot be stored here. However, they can
be obtained by "researchers who can demonstrate that their work is in
the public interest." See the STATA section below.

All files are stored in the file hjp-files.zip. Because this zip file
contains both binary and ASCII files (in DOS format), Unix/Linux users
should be careful.

The replication package has three main folders: STATA, Matlab and Python.


STATA:
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The STATA folder contains all the .do-files used to construct the
empirical measures from the BHSP. All BHPS data can be downloaded free
of charge at https://www.iser.essex.ac.uk/bhps/acquiring-the-data
Running the "main.do" file produces all output required. In that file
the path to the raw BHPS data should be specified along with the path
of the replication package.


Matlab:
------

The following Matlab files produce the structural empirical estimation
results reported in the paper. All results are stored in the "Results"
folder.

main.m: main file. Running this file will run estimation for both
        educational groups and run the post-estimation files to produce the
        output.

run_estimate.m: estimation file. You can run this file by it's own.

run_post_estimation.m: post-estimation production of tables and
        figures. Loads the estimation results from the run_estimate.m file

fun.m: static class used to call all functions to estimate the
        empirical retirement model

mex_combi.mexw64: C++ compiled file calculating the values for all
        combinations of retirement alternatives

The following folders/files are used to estimate the two examples in
the paper:

ex1_Probit: folder containing the Matlab files for generating the
        results for this example. Run the "main_probit.m" file.

ex2_Weibul: folder containing the Matlab files for generating the
        results for this example. Run the "main_weibul.m" file.


Python:
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Probit.ipynb:

  Python notebook that replicates the first (Probit) example in the
  paper. The exact numbers differ slighty due to different random
  numbers drawn in this implementation relative to the Matlab implementation.