getShareInfo {BLPestimatoR} | R Documentation |
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 |
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)
[Package BLPestimatoR version 0.3.4 Index]