Package 'BLPestimatoR'

Title: Performs a BLP Demand Estimation
Description: 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 (aut), Constantin Weiser (ctr), Andre Romahn (ctr)
Maintainer: Daniel Brunner <[email protected]>
License: GPL-3
Version: 0.3.4
Built: 2024-11-19 06:30:46 UTC
Source: CRAN

Help Index

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

Description

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

Usage

BLP_data(
  model,
  market_identifier,
  product_identifier,
  par_delta,
  group_structure = NULL,
  additional_variables = NULL,
  productData,
  demographic_draws,
  integration_accuracy,
  integration_method,
  integration_draws,
  integration_weights,
  integration_seed,
  blp_inner_tol = 1e-09,
  blp_inner_maxit = 10000
)

Arguments

model

the model to be estimated in R's formula syntax,
market_identifier

character specifying the market identifier (variable name must be included in productData),
product_identifier

character specifying the product identifier (variable name must be included in productData),
par_delta

optional: numeric vector with values for the mean utility (variable name must be included in productData),
group_structure

optional: character specifying a group structure for clustered standard erros (variable name must be included in productData),
additional_variables

optional: character vector specifying variables you want to keep for later analysis (variable names must be included in productData)
productData

data.frame with product characteristics,
demographic_draws

optional: list with demographic draws for each market to consider observed heterogeneity (see details),
integration_accuracy

integer specifying integration accuracy,
integration_method

character specifying integration method,
integration_draws

numeric matrix of manually provided integration draws (see details),
integration_weights

numeric vector of manually provided integration weights,
integration_seed

seed for the draws of Monte Carlo based integration,
blp_inner_tol

tolerance for the contraction mapping (default: 1e-9),
blp_inner_maxit

maximum iterations for the contraction mapping (default: 10000)

Details

For any form of user provided integration draws, i.e. integration_draws (unobserved heterogeneity) or demographic_draws (observed heterogeneity), list entries must be named and contain the variable market_identifier to allow market matching. Each line in these list entries contains the draws for one market. In case of unobserved heterogeneity, list names must match the random coefficients from the model formula. The par_delta argument provides the variable name for mean utilitys. For example, in the estimation algorithm these values are used as starting guesses in the contraction mapping. Another example is the evaluation of the GMM, which is also based on the provided mean utilitys. If you need to update par_delta or any other variable in the data object, use update_BLP_data.

Value

Returns an object of class blp_data.

Examples

K<-2 #number of random coefficients
data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20,
                        Xlin = c("price", "x1", "x2", "x3", "x4", "x5"),
                        Xexo = c("x1", "x2", "x3", "x4", "x5"),
                        Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10),
                        true.parameters = list(Xlin.true.except.price = rep(0.2,5),
                                               Xlin.true.price = -0.2,
                                               Xrandom.true = rep(2,K),
                                               instrument.effects = rep(2,10),
                                               instrument.Xexo.effects = rep(1,5)),
                        price.endogeneity = list( mean.xi = -2,
                                                  mean.eita = 0,
                                                  cov = cbind( c(1,0.7), c(0.7,1))),
                        printlevel = 0, seed = 234234 )


model <- as.formula("shares ~  price + x1 + x2 + x3 + x4 + x5 |
                    x1 + x2 + x3 + x4 + x5 |
                    0+ x1 + x2 |
                    iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" )

blp_data <- BLP_data(model = model, market_identifier="cdid",
                     product_id = "prod_id",
                     productData = data,
                     integration_method = "MLHS" ,
                     integration_accuracy = 40,
                     integration_seed = 1)

Draws for observed heterogeneity in Nevo's cereal example.

Description

Draws for observed heterogeneity in Nevo's cereal example.

Usage

demographicData_cereal

Format

Draws for observed heterogeneity for each demographic.

cdid

market identifier,

draws_

20 draws differing across markets.

Source

https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/26803/SOF9FW&version=1.0

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

Description

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

Usage

dstddelta_wrap(blp_data, par_theta2, market, printLevel = 1)

Arguments

blp_data

data object created by the function BLP_data,
par_theta2

matrix with column and rownames providing a starting value for the optimization routine (see details),
market

character specifying the market in which derivatives are calculated,
printLevel

level of output information (default = 1)

Details

NA's in par_theta2 entries indicate the exclusion from estimation, i.e. the coefficient is assumed to be zero. If only unobserved heterogeneity is used (no demographics), the column name of par_theta2 must be "unobs_sd". With demographics the colnames must match the names of provided demographics (as in demographic_draws) and "unobs_sd". Row names of par_theta2 must match random coefficients as specified in model. Constants must be named "(Intercept)".

Value

Returns a numeric matrix with derivatives. Cell in row i and col j is the derivative of share i with respect to mean utility j.

Examples

K<-2 #number of random coefficients
data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20,
                        Xlin = c("price", "x1", "x2", "x3", "x4", "x5"),
                        Xexo = c("x1", "x2", "x3", "x4", "x5"),
                        Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10),
                        true.parameters = list(Xlin.true.except.price = rep(0.2,5),
                                               Xlin.true.price = -0.2,
                                               Xrandom.true = rep(2,K),
                                               instrument.effects = rep(2,10),
                                               instrument.Xexo.effects = rep(1,5)),
                        price.endogeneity = list( mean.xi = -2,
                                                  mean.eita = 0,
                                                  cov = cbind( c(1,0.7), c(0.7,1))),
                        printlevel = 0, seed = 234234 )


model <- as.formula("shares ~  price + x1 + x2 + x3 + x4 + x5 |
                    x1 + x2 + x3 + x4 + x5 |
                    0+ x1 + x2 |
                    iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" )

blp_data <- BLP_data(model = model, market_identifier="cdid",
                     product_id = "prod_id",
                     productData = data,
                     integration_method = "MLHS" ,
                     integration_accuracy = 40,
                     integration_seed = 1)

theta2 <- matrix(c(0.5,2), nrow=2)
rownames(theta2) <- c("x1","x2")
colnames(theta2) <- "unobs_sd"

derivatives2 <- dstddelta_wrap(  blp_data=blp_data,
                                  par_theta2 = theta2,
                                 market = 2)

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

Description

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

Usage

dstdtheta_wrap(blp_data, par_theta2, market, printLevel = 1)

Arguments

blp_data

data object created by the function BLP_data,
par_theta2

matrix with column and rownames providing a starting value for the optimization routine (see details),
market

character specifying the market in which derivatives are calculated,
printLevel

level of output information (default = 1)

Details

NA's in par_theta2 entries indicate the exclusion from estimation, i.e. the coefficient is assumed to be zero. If only unobserved heterogeneity is used (no demographics), the column name of par_theta2 must be "unobs_sd". With demographics the colnames must match the names of provided demographics (as in demographic_draws) and "unobs_sd". Row names of par_theta2 must match random coefficients as specified in model. Constants must be named "(Intercept)".

Value

Returns a numeric matrix with derivatives. Cell in row i and col j is the derivative of share i with respect to parameter j.

Examples

K<-2 #number of random coefficients
data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20,
                        Xlin = c("price", "x1", "x2", "x3", "x4", "x5"),
                        Xexo = c("x1", "x2", "x3", "x4", "x5"),
                        Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10),
                        true.parameters = list(Xlin.true.except.price = rep(0.2,5),
                                               Xlin.true.price = -0.2,
                                               Xrandom.true = rep(2,K),
                                               instrument.effects = rep(2,10),
                                               instrument.Xexo.effects = rep(1,5)),
                        price.endogeneity = list( mean.xi = -2,
                                                  mean.eita = 0,
                                                  cov = cbind( c(1,0.7), c(0.7,1))),
                        printlevel = 0, seed = 234234 )


model <- as.formula("shares ~  price + x1 + x2 + x3 + x4 + x5 |
                    x1 + x2 + x3 + x4 + x5 |
                    0+ x1 + x2 |
                    iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" )

blp_data <- BLP_data(model = model, market_identifier="cdid",
                     product_id = "prod_id",
                     productData = data,
                     integration_method = "MLHS" ,
                     integration_accuracy = 40,
                     integration_seed = 1)

theta2 <- matrix(c(0.5,2), nrow=2)
rownames(theta2) <- c("x1","x2")
colnames(theta2) <- "unobs_sd"

derivatives1 <- dstdtheta_wrap(  blp_data=blp_data,
                                  par_theta2 = theta2,
                                 market = 2)

Ownership matrix in BLP's car example.

Description

Ownership matrix in BLP's car example.

Usage

dummies_cars

Format

Dummy variables.

column i

1, if product in row j is produced by firm i, 0 otherwise

Source

https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/26803/SOF9FW&version=1.0

Performs a BLP demand estimation.

Description

Performs a BLP demand estimation.

Usage

estimateBLP(
  blp_data,
  par_theta2,
  solver_method = "BFGS",
  solver_maxit = 10000,
  solver_reltol = 1e-06,
  standardError = "heteroskedastic",
  extremumCheck = FALSE,
  printLevel = 2,
  ...
)

Arguments

blp_data

data object created by the function BLP_data,
par_theta2

matrix with column and rownames providing a starting value for the optimization routine (see details),
solver_method

character specifying the solver method in optim (further arguments can be passed to optim by ...)
solver_maxit

integer specifying maximum iterations for the optimization routine (default=10000),
solver_reltol

integer specifying tolerance for the optimization routine (default= 1e-6),
standardError

character specifying assumptions about the GMM residual (homoskedastic , heteroskedastic (default), or cluster)
extremumCheck

if TRUE, second derivatives are checked for the existence of minimum at the point estimate (default = FALSE),
printLevel

level of output information ranges from 0 (no GMM results) to 4 (every norm in the contraction mapping)
...

additional arguments for optim

Details

NA's in par_theta2 entries indicate the exclusion from estimation, i.e. the coefficient is assumed to be zero. If only unobserved heterogeneity is used (no demographics), the column name of par_theta2 must be "unobs_sd". With demographics the colnames must match the names of provided demographics (as in demographic_draws) and "unobs_sd". Row names of par_theta2 must match random coefficients as specified in model. Constants must be named "(Intercept)".

Value

Returns an object of class "blp_est". This object contains, among others, all estimates for preference parameters and standard errors.

Examples

K<-2 #number of random coefficients
data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20,
                        Xlin = c("price", "x1", "x2", "x3", "x4", "x5"),
                        Xexo = c("x1", "x2", "x3", "x4", "x5"),
                        Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10),
                        true.parameters = list(Xlin.true.except.price = rep(0.2,5),
                                               Xlin.true.price = -0.2,
                                               Xrandom.true = rep(2,K),
                                               instrument.effects = rep(2,10),
                                               instrument.Xexo.effects = rep(1,5)),
                        price.endogeneity = list( mean.xi = -2,
                                                  mean.eita = 0,
                                                  cov = cbind( c(1,0.7), c(0.7,1))),
                        printlevel = 0, seed = 234234 )


model <- as.formula("shares ~  price + x1 + x2 + x3 + x4 + x5 |
                    x1 + x2 + x3 + x4 + x5 |
                    0+ x1 + x2 |
                    iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" )

blp_data <- BLP_data(model = model, market_identifier="cdid",
                     product_id = "prod_id",
                     productData = data,
                     integration_method = "MLHS" ,
                     integration_accuracy = 40,
                     integration_seed = 1)

theta_guesses <- matrix(c(0.5,2), nrow=2)
rownames(theta_guesses) <- c("x1","x2")
colnames(theta_guesses) <- "unobs_sd"

blp_est <- estimateBLP(blp_data =blp_data,
                       par_theta2 = theta_guesses,
                       extremumCheck = FALSE ,
                       printLevel = 1 )
summary(blp_est)

Calculates elasticities for a given variable and market.

Description

Calculates elasticities for a given variable and market.

Usage

get_elasticities(
  blp_data,
  share_info,
  theta_lin,
  variable,
  products,
  market,
  printLevel = 1
)

Arguments

blp_data

data object created by the function BLP_data,
share_info

object with individual and aggregated choice probabilities created by the function getShareInfo,
theta_lin

linear parameter of the variable for which elasticities are calculated for,
variable

character specifying a variable for which elasticities are calculated for,
products

optional: character vector of specific products,
market

character specifying the market in which elasticities are calculated
printLevel

level of output information (default = 1)

Value

Returns a matrix with elasticities. Value in row j and col i for a variable x, gives the effect of a change in product i's characteristic x on the share of product j.

Examples

K<-2 #number of random coefficients
data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20,
                             Xlin = c("price", "x1", "x2", "x3", "x4", "x5"),
                             Xexo = c("x1", "x2", "x3", "x4", "x5"),
                             Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10),
                             true.parameters = list(Xlin.true.except.price = rep(0.2,5),
                                                    Xlin.true.price = -0.2,
                                                    Xrandom.true = rep(2,K),
                                                    instrument.effects = rep(2,10),
                                                    instrument.Xexo.effects = rep(1,5)),
                             price.endogeneity = list( mean.xi = -2,
                                                       mean.eita = 0,
                                                       cov = cbind( c(1,0.7), c(0.7,1))),
                             printlevel = 0, seed = 234234 )


model <- as.formula("shares ~  price + x1 + x2 + x3 + x4 + x5 |
                    x1 + x2 + x3 + x4 + x5 |
                    0+ x1 + x2 |
                    iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" )

blp_data <- BLP_data(model = model, market_identifier="cdid",
                     product_id = "prod_id",
                     productData = data,
                     integration_method = "MLHS" ,
                     integration_accuracy = 40,
                     integration_seed = 1)

theta_guesses <- matrix(c(0.5,2), nrow=2)
rownames(theta_guesses) <- c("x1","x2")
colnames(theta_guesses) <- "unobs_sd"

shareObj <- getShareInfo(  blp_data=blp_data,
                           par_theta2 = theta_guesses,
                           printLevel = 1)


get_elasticities(blp_data=blp_data,
                 share_info = shareObj ,
                 theta_lin = 1,
                 variable = "price",
                 products = c("4","20"),
                 market = 1)

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

Description

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

Usage

getDelta_wrap(blp_data, par_theta2, printLevel = 1)

Arguments

blp_data

data object created by the function BLP_data,
par_theta2

matrix with column and rownames providing a starting value for the optimization routine (see details),
printLevel

level of output information (default = 1)

Details

NA's in par_theta2 entries indicate the exclusion from estimation, i.e. the coefficient is assumed to be zero. If only unobserved heterogeneity is used (no demographics), the column name of par_theta2 must be "unobs_sd". With demographics the colnames must match the names of provided demographics (as in demographic_draws) and "unobs_sd". Row names of par_theta2 must match random coefficients as specified in model. Constants must be named "(Intercept)".

Starting guesses for the contraction mapping are provided with BLP_data.

Value

Returns an object of class "blp_cm" with results from the contraction mapping.

delta

resulting vector of mean utilities after the contraction mapping

counter

inner iterations needed to convergence

sij

market share integral evaluations for each product (in rows) for the final mean utility

Examples

K<-2 #number of random coefficients
data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20,
                        Xlin = c("price", "x1", "x2", "x3", "x4", "x5"),
                        Xexo = c("x1", "x2", "x3", "x4", "x5"),
                        Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10),
                        true.parameters = list(Xlin.true.except.price = rep(0.2,5),
                                               Xlin.true.price = -0.2,
                                               Xrandom.true = rep(2,K),
                                               instrument.effects = rep(2,10),
                                               instrument.Xexo.effects = rep(1,5)),
                        price.endogeneity = list( mean.xi = -2,
                                                  mean.eita = 0,
                                                  cov = cbind( c(1,0.7), c(0.7,1))),
                        printlevel = 0, seed = 234234 )


model <- as.formula("shares ~  price + x1 + x2 + x3 + x4 + x5 |
                    x1 + x2 + x3 + x4 + x5 |
                    0+ x1 + x2 |
                    iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" )

blp_data <- BLP_data(model = model, market_identifier="cdid",
                     product_id = "prod_id",
                     productData = data,
                     integration_method = "MLHS" ,
                     integration_accuracy = 40,
                     integration_seed = 1)

theta_guesses <- matrix(c(0.5,2), nrow=2)
rownames(theta_guesses) <- c("x1","x2")
colnames(theta_guesses) <- "unobs_sd"

delta_eval <- getDelta_wrap(  blp_data=blp_data,
                              par_theta2 = theta_guesses,
                              printLevel = 4)

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

Description

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

Usage

getJacobian_wrap(blp_data, par_theta2, printLevel = 1)

Arguments

blp_data

data object created by the function BLP_data,
par_theta2

matrix with column and rownames providing the evaluation point (see details),
printLevel

level of output information (default = 1)

Details

NA's in par_theta2 entries indicate the exclusion from estimation, i.e. the coefficient is assumed to be zero. If only unobserved heterogeneity is used (no demographics), the column name of par_theta2 must be "unobs_sd". With demographics the colnames must match the names of provided demographics (as in demographic_draws) and "unobs_sd". Row names of par_theta2 must match random coefficients as specified in model. Constants must be named "(Intercept)".

Value

Returns a matrix with the jacobian (products in rows, parameters in columns).

Examples

K<-2 #number of random coefficients
data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20,
                        Xlin = c("price", "x1", "x2", "x3", "x4", "x5"),
                        Xexo = c("x1", "x2", "x3", "x4", "x5"),
                        Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10),
                        true.parameters = list(Xlin.true.except.price = rep(0.2,5),
                                               Xlin.true.price = -0.2,
                                               Xrandom.true = rep(2,K),
                                               instrument.effects = rep(2,10),
                                               instrument.Xexo.effects = rep(1,5)),
                        price.endogeneity = list( mean.xi = -2,
                                                  mean.eita = 0,
                                                  cov = cbind( c(1,0.7), c(0.7,1))),
                        printlevel = 0, seed = 234234 )


model <- as.formula("shares ~  price + x1 + x2 + x3 + x4 + x5 |
                    x1 + x2 + x3 + x4 + x5 |
                    0+ x1 + x2 |
                    iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" )

blp_data <- BLP_data(model = model, market_identifier="cdid",
                     product_id = "prod_id",
                     productData = data,
                     integration_method = "MLHS" ,
                     integration_accuracy = 40,
                     integration_seed = 1)

theta_guesses <- matrix(c(0.5,2), nrow=2)
rownames(theta_guesses) <- c("x1","x2")
colnames(theta_guesses) <- "unobs_sd"

jacobian <- getJacobian_wrap(blp_data=blp_data,
                             par_theta2 = theta_guesses,
                             printLevel = 2)
head(jacobian)

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

Description

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

Usage

getShareInfo(blp_data, par_theta2, printLevel = 1)

Arguments

blp_data

data object created by the function BLP_data (provides, among others, mean utilitys and integration draws),
par_theta2

matrix with column and rownames providing the evaluation point (see details),
printLevel

level of output information (default = 1)

Value

Returns a list with information related to predicted shares.

Examples

K<-2 #number of random coefficients
data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20,
                        Xlin = c("price", "x1", "x2", "x3", "x4", "x5"),
                        Xexo = c("x1", "x2", "x3", "x4", "x5"),
                        Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10),
                        true.parameters = list(Xlin.true.except.price = rep(0.2,5),
                                               Xlin.true.price = -0.2,
                                               Xrandom.true = rep(2,K),
                                               instrument.effects = rep(2,10),
                                               instrument.Xexo.effects = rep(1,5)),
                        price.endogeneity = list( mean.xi = -2,
                                                  mean.eita = 0,
                                                  cov = cbind( c(1,0.7), c(0.7,1))),
                        printlevel = 0, seed = 234234 )

model <- as.formula("shares ~  price + x1 + x2 + x3 + x4 + x5 |
                    x1 + x2 + x3 + x4 + x5 |
                    0+ x1 + x2 |
                    iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" )

blp_data <- BLP_data(model = model, market_identifier="cdid",
                     product_id = "prod_id",
                     productData = data,
                     integration_method = "MLHS" ,
                     integration_accuracy = 40,
                     integration_seed = 1)

theta_guesses <- matrix(c(0.5,2), nrow=2)
rownames(theta_guesses) <- c("x1","x2")
colnames(theta_guesses) <- "unobs_sd"

shares <- getShareInfo(  blp_data=blp_data,
                           par_theta2 = theta_guesses,
                           printLevel = 4)

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

Description

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

Usage

gmm_obj_wrap(blp_data, par_theta2, printLevel = 2)

Arguments

blp_data

data object created by the function BLP_data,
par_theta2

matrix with column and rownames providing a starting value for the optimization routine (see details),
printLevel

level of output information ranges from 1 (no GMM results) to 4 (every norm in the contraction mapping)

Details

NA's in par_theta2 entries indicate the exclusion from estimation, i.e. the coefficient is assumed to be zero. If only unobserved heterogeneity is used (no demographics), the column name of par_theta2 must be "unobs_sd". With demographics the colnames must match the names of provided demographics (as in demographic_draws) and "unobs_sd". Row names of par_theta2 must match random coefficients as specified in model. Constants must be named "(Intercept)".

Value

Returns a list with results from the GMM evaluation.

local_min

GMM point evaluation

gradient

GMM derivative with respect to non-linear parameters

delta

result of the contraction mapping

xi

residuals of GMM evaluation

Examples

K<-2 #number of random coefficients
data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20,
                        Xlin = c("price", "x1", "x2", "x3", "x4", "x5"),
                        Xexo = c("x1", "x2", "x3", "x4", "x5"),
                        Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10),
                        true.parameters = list(Xlin.true.except.price = rep(0.2,5),
                                               Xlin.true.price = -0.2,
                                               Xrandom.true = rep(2,K),
                                               instrument.effects = rep(2,10),
                                               instrument.Xexo.effects = rep(1,5)),
                        price.endogeneity = list( mean.xi = -2,
                                                  mean.eita = 0,
                                                  cov = cbind( c(1,0.7), c(0.7,1))),
                        printlevel = 0, seed = 234234 )


model <- as.formula("shares ~  price + x1 + x2 + x3 + x4 + x5 |
                    x1 + x2 + x3 + x4 + x5 |
                    0+ x1 + x2 |
                    iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" )

blp_data <- BLP_data(model = model, market_identifier="cdid",
                     product_id = "prod_id",
                     productData = data,
                     integration_method = "MLHS" ,
                     integration_accuracy = 40,
                     integration_seed = 1)

theta_guesses <- matrix(c(0.5,2), nrow=2)
rownames(theta_guesses) <- c("x1","x2")
colnames(theta_guesses) <- "unobs_sd"

gmm <- gmm_obj_wrap(  blp_data=blp_data,
                      par_theta2 = theta_guesses,
                      printLevel = 2)
gmm$local_min

Draws for unobserved heterogeneity in Nevo's cereal example.

Description

Draws for unobserved heterogeneity in Nevo's cereal example.

Usage

originalDraws_cereal

Format

Each list entry contains draws (unobserved heterogeneity) for a random coefficient.

cdid

market identifier,

draws_

20 draws differing across markets.

Source

https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/26803/SOF9FW&version=1.0

Product data of BLP's car example.

Description

Product data of BLP's car example.

Usage

productData_cars

Format

A data frame with product data of 2217 cars in 20 markets.

share

car market share,

price

car price,

hpwt

horsepower-weight ratio,

air

1, if car has air conditioning, 0 otherwise,

mpg

market identifier,

space

length times width of the car,

const

constant,

id

uniquely identifies a car,

cdid

uniquely identifies the market of a product,

firmid

uniquely identifies the firm of a product (corresponds to column number in the ownership matrix).

Source

https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/26803/SOF9FW&version=1.0

Product data of Nevo's cereal example.

Description

Product data of Nevo's cereal example.

Usage

productData_cereal

Format

A data frame with product data of 24 cereals in each of 94 markets.

share

cereals market share,

price

cereals price,

const

constant,

sugar

cereals sugar,

mushy

cereals mushy,

cdid

market identifier,

product_id

uniquely identifies a product in a market,

productdummy

uniquely identifies a product in a market,

IV1

1. instrument,

IV2

2. instrument,

IV3

3. instrument,

IV4

4. instrument,

IV5

5. instrument,

IV6

6. instrument,

IV7

7. instrument,

IV8

8. instrument,

IV9

9. instrument,

IV10

10. instrument,

IV11

11. instrument,

IV12

12. instrument,

IV13

13. instrument,

IV14

14. instrument,

IV15

15. instrument,

IV16

16. instrument,

IV17

17. instrument,

IV18

18. instrument,

IV19

19. instrument,

IV20

20. instrument

Source

https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/26803/SOF9FW&version=1.0

This function creates a simulated BLP dataset.

Description

This function creates a simulated BLP dataset.

Usage

simulate_BLP_dataset(
  nmkt,
  nbrn,
  Xlin,
  Xexo,
  Xrandom,
  instruments,
  true.parameters = list(),
  price.endogeneity = list(mean.xi = -2, mean.eita = 0, cov = cbind(c(1, 0.7), c(0.7,
    1))),
  printlevel = 1,
  seed
)

Arguments

nmkt

number of markets
nbrn

number of products
Xlin

character vector specifying the set of linear variables
Xexo

character vector specifying the set of exogenous variables (subset of Xlin)
Xrandom

character vector specifying the set of random coefficients (subset of Xlin)
instruments

character vector specifying the set of instrumental variables
true.parameters

list with parameters of the DGP

Xlin.true.except.price

"true" linear coefficients in utility function except price

Xlin.true.price

"true" linear price coefficient in utility function

Xrandom.true

"true" set of random coefficients

instrument.effects

"true" coefficients of instrumental variables to explain endogenous price

instrument.Xexo.effects

"true" coefficients of exogenous variables to explain endogenous price
price.endogeneity

list with arguments of the multivariate normal distribution

mean.xi

controls for the mean of the error term in the utility function

mean.eita

controls for the mean of the error term in the price function

cov

controls for the covariance of xi and eita

printlevel

0 (no output) 1 (summary of generated data)
seed

seed for the random number generator

Details

The dataset is balanced, so every market has the same amount of products. Only unobserved heterogeneity can be considered. Variables that enter the equation as a Random Coefficient or exogenously must be included in the set of linear variables. The parameter.list argument specifies the "true" effect on the individual utility for each component. Prices are generated endogenous as a function of exogenous variables and instruments, where the respective effect sizes are specified in instrument.effects and instrument.Xexo.effects. Error terms xi and eita are drawn from a multivariate normal distribution, whose parameters can be set in price.endogeneity. Market shares are generated by MLHS integration rule with 10000 nodes.

Value

Returns a simulated BLP dataset.

Examples

K<-2 #number of random coefficients

Parameter starting guesses for Nevo's cereal example.

Description

Parameter starting guesses for Nevo's cereal example.

Usage

theta_guesses_cereal

Format

A matrix with 4 random coefficients (rows) and columns for 4 demographics and one unobserved heterogeneity column (5 cols in total).

Source

https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/26803/SOF9FW&version=1.0

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

Description

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

Usage

update_BLP_data(data_update, blp_data)

Arguments

data_update

data.frame with variables to update (must contain the market_identifier and product_identifier variables as in blp_data),
blp_data

data object created by the function BLP_data

Value

Returns an object of class blp_data.

Examples

K<-2 #number of random coefficients
data <- simulate_BLP_dataset(nmkt = 25, nbrn = 20,
                        Xlin = c("price", "x1", "x2", "x3", "x4", "x5"),
                        Xexo = c("x1", "x2", "x3", "x4", "x5"),
                        Xrandom = paste0("x",1:K),instruments = paste0("iv",1:10),
                        true.parameters = list(Xlin.true.except.price = rep(0.2,5),
                                               Xlin.true.price = -0.2,
                                               Xrandom.true = rep(2,K),
                                               instrument.effects = rep(2,10),
                                               instrument.Xexo.effects = rep(1,5)),
                        price.endogeneity = list( mean.xi = -2,
                                                  mean.eita = 0,
                                                  cov = cbind( c(1,0.7), c(0.7,1))),
                        printlevel = 0, seed = 234234 )


model <- as.formula("shares ~  price + x1 + x2 + x3 + x4 + x5 |
                    x1 + x2 + x3 + x4 + x5 |
                    0+ x1 + x2 |
                    iv1 + iv2 + iv3 + iv4 + iv5 + iv6 + iv7 + iv8 +iv9 +iv10" )

blp_data <- BLP_data(model = model, market_identifier="cdid",
                     product_id = "prod_id",
                     productData = data,
                     integration_method = "MLHS" ,
                     integration_accuracy = 40,
                     integration_seed = 1)

new_data <- data.frame(price = seq(1,10,length.out=500),
                       x1 =  seq(2,10,length.out=500),
                       cdid = sort(rep(1:25,20)),
                       prod_id = rep(1:20,25) )
blp_data_example_updated <-update_BLP_data(blp_data = blp_data,
                                           data_update = new_data)

Mean utility starting guesses for Nevo's cereal example.

Description

Mean utility starting guesses for Nevo's cereal example.

Usage

w_guesses_cereal

Format

A numeric vector of 2256 values.

Source

https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/26803/SOF9FW&version=1.0