BLPestimatoR (version 0.3.4)

Performs a BLP Demand Estimation

Description

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

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Version

Install

install.packages('BLPestimatoR')

Monthly Downloads

371

Version

0.3.4

License

GPL-3

Maintainer

Daniel Brunner

Last Published

December 3rd, 2022

Functions in BLPestimatoR (0.3.4)

w_guesses_cereal

Mean utility starting guesses for Nevo's cereal example.

gmm_obj_wrap

Calculating the GMM objective for a given set of non-linear parameters.

update_BLP_data

Updates the set of linear, exogenous, random coefficient, share or mean utility variable in the data object.

simulate_BLP_dataset

This function creates a simulated BLP dataset.

originalDraws_cereal

Draws for unobserved heterogeneity in Nevo's cereal example.

productData_cars

Product data of BLP's car example.

theta_guesses_cereal

Parameter starting guesses for Nevo's cereal example.

productData_cereal

Product data of Nevo's cereal example.

getShareInfo

Calculates information related to predicted shares for a given set of non-linear parameters and data.

getJacobian_wrap

Calculating the Jacobian for a given set of non-linear parameters and mean utilities.

estimateBLP

Performs a BLP demand estimation.

get_elasticities

Calculates elasticities for a given variable and market.

dstddelta_wrap

Calculates derivatives of all shares with respect to all mean utilities in a given market.

getDelta_wrap

Performs a contration mapping for a given set of non-linear parameters.

dstdtheta_wrap

Calculates derivatives of all shares with respect to all non-linear parameters in a given market.

demographicData_cereal

Draws for observed heterogeneity in Nevo's cereal example.

dummies_cars

Ownership matrix in BLP's car example.

BLP_data

Prepares data and parameters related to the BLP algorithm for estimation.