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rblp: BLP Demand Estimation for Differentiated Products

Source: R/rblp-package.R
rblp-package.Rd

Estimate, simulate, and analyze demand for differentiated products using BLP-type random coefficients logit models (Berry, Levinsohn, and Pakes, 1995).

Details

The rblp package provides tools for:

  • Estimating random coefficients logit demand models via GMM

  • Nested logit and random coefficients nested logit

  • Joint demand and supply estimation

  • Post-estimation analysis: elasticities, diversion ratios, markups

  • Merger simulation and counterfactual analysis

  • Consumer surplus computation

  • Optimal instrument construction

  • Micro moments (Conlon and Gortmaker, 2025)

  • Data simulation for Monte Carlo studies

The main workflow is:

  1. Create formulations with blp_formulation

  2. Set up integration with blp_integration

  3. Define the problem with blp_problem

  4. Estimate with problem$solve()

  5. Analyze with result methods (e.g., results$compute_elasticities())

Translated from the pyblp Python package by Conlon and Gortmaker (2020).

References

Berry, S., Levinsohn, J., & Pakes, A. (1995). Automobile Prices in Market Equilibrium. Econometrica, 63(4), 841-890.

Conlon, C., & Gortmaker, J. (2020). Best Practices for Differentiated Products Demand Estimation with PyBLP. RAND Journal of Economics, 51(4), 1108-1161.

Nevo, A. (2000). A Practitioner's Guide to Estimation of Random-Coefficients Logit Models of Demand. Journal of Economics & Management Strategy, 9(4), 513-548.

See also

Useful links:

Author

Maintainer: Hyoungchul Kim hchulkim@virginia.edu