Package: BLPestimatoR 0.3.4

BLPestimatoR: Performs a BLP Demand Estimation

Provides the estimation algorithm to perform the demand estimation described in Berry, Levinsohn and Pakes (1995) <doi:10.2307/2171802> . The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines.

Authors:Daniel Brunner, Constantin Weiser, Andre Romahn

# Install 'BLPestimatoR' in R:

install.packages('BLPestimatoR', repos = c('https://danielbr2.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

demographicData_cereal - Draws for observed heterogeneity in Nevo's cereal example.
dummies_cars - Ownership matrix in BLP's car example.
originalDraws_cereal - Draws for unobserved heterogeneity in Nevo's cereal example.
productData_cars - Product data of BLP's car example.
productData_cereal - Product data of Nevo's cereal example.
theta_guesses_cereal - Parameter starting guesses for Nevo's cereal example.
w_guesses_cereal - Mean utility starting guesses for Nevo's cereal example.

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas cpp

3.26 score 8 stars 23 scripts 431 downloads 11 exports 11 dependencies

Last updated 2 years agofrom:0b10d356bd. Checks:12 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 20 2025
R-4.5-win-x86_64	OK	Mar 20 2025
R-4.5-mac-x86_64	OK	Mar 20 2025
R-4.5-mac-aarch64	OK	Mar 20 2025
R-4.5-linux-x86_64	OK	Mar 20 2025
R-4.4-win-x86_64	OK	Mar 20 2025
R-4.4-mac-x86_64	OK	Mar 20 2025
R-4.4-mac-aarch64	OK	Mar 20 2025
R-4.4-linux-x86_64	OK	Mar 20 2025
R-4.3-win-x86_64	OK	Mar 20 2025
R-4.3-mac-x86_64	OK	Mar 20 2025
R-4.3-mac-aarch64	OK	Mar 20 2025

Exports:BLP_data dstddelta_wrap dstdtheta_wrap estimateBLP get_elasticities getDelta_wrap getJacobian_wrap getShareInfo gmm_obj_wrap simulate_BLP_dataset update_BLP_data

Dependencies:data.table Formula lattice Matrix mvQuad numDeriv randtoolbox Rcpp RcppArmadillo rngWELL statmod

BLPestimatoR - Package for Demand Estimation

Daniel Brunner

Rendered fromblp_intro.Rmdusingknitr::knitron Mar 20 2025.

Last update: 2022-12-03
Started: 2018-08-16

Citation

Readme and manuals

Help Manual

Help page	Topics
Prepares data and parameters related to the BLP algorithm for estimation.	BLP_data
Draws for observed heterogeneity in Nevo's cereal example.	demographicData_cereal
Calculates derivatives of all shares with respect to all mean utilities in a given market.	dstddelta_wrap
Calculates derivatives of all shares with respect to all non-linear parameters in a given market.	dstdtheta_wrap
Ownership matrix in BLP's car example.	dummies_cars
Performs a BLP demand estimation.	estimateBLP
Calculates elasticities for a given variable and market.	get_elasticities
Performs a contration mapping for a given set of non-linear parameters.	getDelta_wrap
Calculating the Jacobian for a given set of non-linear parameters and mean utilities.	getJacobian_wrap
Calculates information related to predicted shares for a given set of non-linear parameters and data.	getShareInfo
Calculating the GMM objective for a given set of non-linear parameters.	gmm_obj_wrap
Draws for unobserved heterogeneity in Nevo's cereal example.	originalDraws_cereal
Product data of BLP's car example.	productData_cars
Product data of Nevo's cereal example.	productData_cereal
This function creates a simulated BLP dataset.	simulate_BLP_dataset
Parameter starting guesses for Nevo's cereal example.	theta_guesses_cereal
Updates the set of linear, exogenous, random coefficient, share or mean utility variable in the data object.	update_BLP_data
Mean utility starting guesses for Nevo's cereal example.	w_guesses_cereal