Professor Francesca Molinari Research
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Item Discrete Choice Under Risk with Limited Consideration in MatlabBarseghyan, Levon; Molinari, Francesca; Thirkettle, Matthew (2021-10)We present the discrete choice under risk with limited consideration MATLAB package. The package implements the method to identify and carry out inference in models of decision making under risk with limited consideration as proposed by Barseghyan, Molinari, and Thirkettle (2021). This manual provides details on how to use the package to replicate the empirical application in Sections V of Barseghyan et al. (2021). Access to the data can be requested from the authors (see required agreement in the ZIP file).Item Heterogeneous Choice Sets and Preferences in MatlabBarseghyan, Levon; Coughlin, Maura; Molinari, Francesca; Teitelbaum, Joshua C. (2021-10)We present the heterogeneous choice sets and preferences MATLAB package implementing the method to construct confidence sets on preference parameters in the presence of unobserved heterogeneity in choice sets proposed by Barseghyan, Coughlin, Molinari, and Teitelbaum (2021). This manual provides details on how to use the package to replicate the empirical application and simulation results in the paper. The version of this code included in this ZIP file is what was used to carry out the empirical application in Sections 4-5 of Barseghyan et al. (2021) and the simulations in Section 6, as well as the related exercises in the Supplement. The data used for the empirical application in Sections 4-5 is proprietary. Access to the data can be requested from the authors (see required agreement in the ZIP file).Item Calibrated Projection in MATLABKaido, Hiroaki; Molinari, Francesca; Stoye, Joerg; Thirkettle, Matthew (2019-03-08)We present the calibrated-projection MATLAB package implementing the method to construct confidence intervals proposed by Kaido, Molinari, and Stoye (2019). This manual provides details on how to use the package for inference on projections of partially identified parameters and instructions on how to replicate the empirical application and simulation results in the paper. The version of this code included in this ZIP file is what was used to carry out the empirical application in Section 4 of Kaido et al. (2019) and the Monte Carlo simulations in Appendix C. Please visit https://molinari.economics.cornell.edu/programs.html for the most up-to-date version of the code.