Python code for BLP (Berry, Levinsohn and Pakes) method of structural demand estimation using the random-coefficients logit model. Code for estimation of demand and supply-side moment jointly is also provided.
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README.md

BLP-Demand-Estimation

Method of structural demand estimation using random-coefficients logit model of Berry, Levinsohn and Pakes (1995). With an example that replicates the results from Nevo (2000b). Data files and variable description were borrowed from Bronwyn Hall.

I would like to thank Prof. Nevo for making his MATLAB code available, which this program is based on.

The program consists of the following files:

  • BLP_notes.pdf - file that explains motivation to use BLP, necessary data, model primitives, and estimation steps.
  • BLP_demand.py - main file that defines BLP class, and using Data class runs the model and outputs coefficient estimates, standard errors and value of objective function.
  • data_Nevo.py - file that defines Data class for Nevo (2000b) replication.
  • iv.mat, ps2.mat - data files for Nevo (2000b) replication.

To run the program:

  1. Run data_Nevo.py
  2. Run BLP_demand.py

Results:

               Mean        SD      Income   Income^2       Age      Child
Constant  -2.010197  0.558228    2.292376   0.000000  1.284481   0.000000
           0.327063  0.162589    1.209040   0.000000  0.631174   0.000000
   Price -62.748438  3.313584  588.675608 -30.210302  0.000000  11.053792
          14.810492  1.340932  270.580464  14.108625  0.000000   4.122544
   Sugar   0.116253 -0.005793   -0.385094   0.000000  0.052249   0.000000
           0.016040  0.013507    0.121525   0.000000  0.025993   0.000000
   Mushy   0.499535  0.093504    0.747860   0.000000 -1.353348   0.000000
           0.198620  0.185438    0.802441   0.000000  0.667079   0.000000
GMM objective: 4.561516417264547
Min-Dist R-squared: 0.4591044701135003
Min-Dist weighted R-squared: 0.10118275148991385

Note: Results differ from those in Nevo (2000b). This is because this code uses a tight tolerance for the contraction mapping. It minimizes the GMM objective function to 4.56, while Nevo's matlab code minimizes to 14.9.

By modifying Data class in data_Nevo.py, one can estimate random-coefficients logit model for any other data set.