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MATLAB code to obtain confidence intervals for projections of partially identified parameters.

Click here to download a zip file containing Matlab programs to replicate the simulation results for the Monte Carlo exercises appearing in Kaido, Molinari and Stoye (2017). The code is "portable" -- the modifications needed for other applications (e.g., providing different studentized sample moment functions and estimators of the population gradients) are minimal and clearly documented in Kaido, Molinari, Stoye and Thirkettle (2017). This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of merchantability or fitness for a particular purpose. If you use the software, we ask that you please cite Kaido, Molinari and Stoye (2017) as the source of the theoretical results and of the code.

Fortran code obtaining sharp identification regions in models with convex moment predictions.

Click here to download a zip file containing Fortran and Matlab programs to replicate the simulation results appearing in Beresteanu, Molchanov and Molinari (2011). This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of merchantability or fitness for a particular purpose. If you use the software, we ask that you please cite Beresteanu, Molchanov and Molinari (Econometrica, 2011) as the source of the theoretical results and the Online Supplement as the source of the code.

STATA Software for Best Linear Prediction with Interval Outcome Data

Click here to download a zip file containing STATA .ado files and .sthlp files to replicate the results in Beresteanu and Molinari (2008) and implement their theoretical results. The code allows for best linear prediction with any number of perfectly observed covariates. It returns estimates of the sharp identification region for each component of the BLP parameter vector, as well as confidence sets and confidence collections for each of these regions. It also allows for hypothesis testing and confidence statements on any pair of (functions of) elements of the BLP parameter vector. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of merchantability or fitness for a particular purpose. If you use the software, we ask that you please cite it as: Arie Beresteanu, Francesca Molinari and Darcy Steeg Morris (2010): "Asymptotics for Partially Identified Models in STATA," and that you also cite Beresteanu and Molinari (Econometrica, 2008) as the source of the theoretical results.

This research was in part supported by the National Science Foundation through a series of grants from 2006-2014 and 2018-present. Any opinions, findings and conclusions or recommendations expressed in these materials are those of the authors and do not necessarily reflect the views of the National Science Foundation.

©2014 Francesca Molinari