.Rhistory[Conflict 1]

print(system.time(jacobian_res <<- jacobian(as.vector(dat[, "delta"]), theta2)))
print(paste0("Updated gradient:", Sys.time()))
print(f <- -2 * as.numeric(t(jacobian_res) %*% PZ %*% xi.hat));
#######
L <- ncol(Z)
covg <- matrix(0, nrow = L, ncol = L)
for(i in 1:JT){
covg <- covg + (Z[i, ] %*% t(Z[i, ])) * xi.hat[i]^2
}
d.delta <- jacobian_res;
Dg <- t(d.delta) %*% Z
p.Dg <- try(solve(Dg %*% W.inv %*% t(Dg)))
cov.mat <- p.Dg %*% (Dg %*% W.inv %*% covg %*% W.inv %*% t(Dg)) %*% p.Dg
beta.est$sigma.se <<- sqrt(diag(cov.mat));
print(paste0("Updated coefficients table:", Sys.time()))
print(beta.est)
write.csv(beta.est, file = paste0("BLP_beta_est_", Sys.Date(), ".csv"))
#######
return(as.numeric(f))
}
#Set up data
dat <- dat[dat[, share.fld] > 0, ]
dat <- dat[order(dat[, mkt.id.fld], dat[, prod.id.fld]), ]
JT <- nrow(dat)
#market identifier variable
mkt.id <- dat[, mkt.id.fld];
#Number of characteristics (including constant and price)
X <- as.matrix(cbind(ones = rep(1, JT), dat[, c(x.var.flds, prc.fld)]));
K <- ncol(X)
#Compute the outside good market share by market
s.jt <- as.vector(dat[, share.fld]);
temp <- aggregate(s.jt, by = list(mkt.id = mkt.id), sum);
sum1 <- temp$x[match(mkt.id, temp$mkt.id)];
s.j0 <- as.vector(1 - sum1);
rm(temp, sum1);
dat[, "delta"] <- Y <- log(s.jt) - log(s.j0);
iv <- dat[, prc.iv.flds]
while(!require(AER)){install.packages("AER")}
#Construct 2SLS regression specification
str.ivreg.y <- "delta ~ "
str.ivreg.x <- paste(x.var.flds, collapse = " + ")
str.ivreg.prc <- paste(prc.fld, collapse = " + ")
str.ivreg.iv <- paste(prc.iv.flds, collapse = " + ")
print("2SLS specification:")
print(fm.ivreg <- paste0(str.ivreg.y, str.ivreg.x, " + ", str.ivreg.prc, " | ", str.ivreg.x, " + ", str.ivreg.iv))
rm(str.ivreg.y, str.ivreg.x, str.ivreg.prc, str.ivreg.iv)
print("2SLS beta estimate:")
print(summary(mo.ivreg <- ivreg(fm.ivreg, data = dat, x = TRUE)))
beta.est <- summary(mo.ivreg)$coef[, 1:2]
#Z = instrumental variable matrix include exogenous X's
Z <- as.matrix(mo.ivreg$x$instruments)
PZ <- Z %*% solve(t(Z) %*% Z) %*% t(Z);
theta1 <- coef(mo.ivreg);
xi.hat <- as.vector(mo.ivreg$resid);
Z.hat <- Z * matrix(rep(xi.hat, ncol(Z)), ncol = ncol(Z))
W.inv <- try(solve(t(Z.hat) %*% Z.hat), silent = FALSE)
if("matrix" == class(W.inv)){
PZ <- Z %*% W.inv %*% t(Z);
PX.inv <- solve(t(X) %*% PZ %*% X)
theta1 <- PX.inv %*% t(X) %*% PZ %*% Y
xi.hat <- Y - X %*% theta1
X.hat <- (PZ %*% X) * matrix(rep(xi.hat, K), ncol = K)
tsls.se <- sqrt(diag(PX.inv %*% t(X.hat) %*% X.hat %*% PX.inv))
print("GMM step 2 updated theta1 estimate:")
print(beta.est <- data.frame(beta.est = theta1, se.est = tsls.se))
}
dat[, "xi.hat"] <- xi.hat
#Starting point
print("Sigma guess:")
if(missing(sigma.guess)){
tsls.se <- beta.est[, 2]
print(theta2 <- 0.5 * tsls.se);
} else {
print(theta2 <- sigma.guess);
}
theta2.prev <- theta2;
# Matrix of individuals' characteristics
#	Standard normal distribution draws, one for each characteristic
v <- matrix(rnorm(K * n.sim), nrow = K, ncol = n.sim)
# Break X and v matrices into list variables
# in attempt to expedite calculation of the Jacobian matrix
l.X <- lapply(1:K, function(k){
return(X[, k])
})
l.v <- lapply(1:K, function(k){
return(v[k, ])
})
l.Xv <- lapply(1:K, function(k){
l.X[[k]] %*% t(l.v[[k]]);
})
print("Estimating random coefficients multinomial logit")
a <- 0;
beta.est <- NULL;
while(!require(SQUAREM)){install.packages("SQUAREM")}
while(!require(BB)){install.packages("BB")}
print(system.time(
theta.est <- multiStart(par = theta2, fn = gmm_obj, gr = gradient_obj, lower = 0, control = list(trace = TRUE, checkGrad=FALSE), action = "optimize")
));
save(theta.est, file = paste0("theta_est_", Sys.time(), ".RData"))
print("Final coefficients estimate:")
theta2 <- theta.est$par
theta2 <- as.numeric(theta2)
gmm.res <- gmm_obj(theta2)
grad.res <- gradient_obj(theta2)
return(list(coef.mat = beta.est, gmm.obj.func = gmm.res, gmm_est = theta.est, final.data = dat))
}
results <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results)
View(jacobian_res)
mean(jacobian_res[,1])
mean(jacobian_res[,2])
mean(jacobian_res[,3])
mean(jacobian_res[,4])
mean(jacobian_res[,5])
results1 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results1)
results1$coef.mat
plot(results[["final.data"]])
plot(results$final.data$delta)
hist(results$final.data$delta)
hist(results$final.data$xi.hat)
plot(results$final.data$xi.hat)
results[["coef.mat"]][["sigma.est"]]
BerryLevinsohnPakes <- function(dat, mkt.id.fld = "mkt.id", prod.id.fld = "prod.id", prc.fld = "px", share.fld = "share", x.var.flds = c("x1", "x2", "x3"), prc.iv.flds = "z", n.sim = 200, sigma.guess){
# all data should appear in the data.frame "dat"
# input variables ending in ".fld" are the names of the columns
# n.sim = number of simulated "indviduals" per market
# sigma guess may be missing
#
# Required packages (installed as the function runs):
# SQUAREM, BB, AER
#
# Based on code written by Aviv Nevo, May 1998.
# Adapted by Michael Carniol, January 2015
blp_inner <- function(delta.in, mu.in) {
# Computes a single update of the BLP (1995) contraction mapping.
# of market level predicted shares.
# This single-update function is required by SQUAREM, see Varadhan and
# Roland (SJS, 2008), and Roland and Varadhan (ANM, 2005)
# INPUT
# 	delta.in : current value of delta vector
# 	mu.in: current mu matrix
# Requires global variables: s.jt
# OUTPUT
# 	delta.out : delta vector that equates observed with predicted market shares
pred.s <- rowMeans(ind_sh(delta.in, mu.in));
delta.out <- delta.in + log(s.jt) - log(pred.s)
return(delta.out)
}
ind_sh <- function(delta.in, mu.in){
# This function computes the "individual" probabilities of choosing each brand
# Requires global variables: mkt.id, X, v
numer <- exp(mu.in) * matrix(rep(exp(delta.in), n.sim), ncol = n.sim);
denom <- as.matrix(do.call("rbind", lapply(mkt.id, function(tt){
1 + colSums(numer[mkt.id %in% tt, ])
})))
return(numer / denom);
}
gmm_obj <- function(theta2){
# This function computes the GMM objective function
# Requires global variable inputs: X, v, delta, a, W
# Outputs: theta1, xi.hat
print(paste0("GMM Loop number: ", Sys.time()))
print(a <<- a + 1)
print("Updated theta2 estimate:")
print(theta2)
print("Change in theta2 estimate:")
print(theta.chg <- as.numeric(theta2 - theta2.prev));
if(sum(theta.chg != 0) <= 2){
delta <- dat[, "delta"];
} else {
delta <- Y;
}
theta2.prev <<- theta2;
mu <- X %*% diag(theta2) %*% v;
print("Running SQUAREM contraction mapping")
print(system.time(
squarem.output <- squarem(par = delta, fixptfn = blp_inner, mu.in = mu, control = list(trace = TRUE))
));
delta <- squarem.output$par
print(summary(dat[, "delta"] - delta));
dat[, "delta"] <<- delta;
mo.ivreg <- ivreg(fm.ivreg, data = dat, x = TRUE)
theta1 <<- coef(mo.ivreg);
xi.hat <<- as.vector(mo.ivreg$resid);
Z.hat <- Z * matrix(rep(xi.hat, ncol(Z)), ncol = ncol(Z))
W.inv <- try(solve(t(Z.hat) %*% Z.hat), silent = FALSE)
if("matrix" == class(W.inv)){
PZ <<- Z %*% W.inv %*% t(Z);
PX.inv <- solve(t(X) %*% PZ %*% X)
theta1 <<- PX.inv %*% t(X) %*% PZ %*% delta
xi.hat <<- delta - X %*% theta1
X.hat <- (PZ %*% X) * matrix(rep(xi.hat, K), ncol = K)
tsls.se <- sqrt(diag(PX.inv %*% t(X.hat) %*% X.hat %*% PX.inv))
print("GMM step 2 updated theta1 estimate:")
print(beta.est <<- data.frame(beta.est = theta1, beta.se = tsls.se, sigma.est = theta2))
}
dat[, "xi.hat"] <<- xi.hat
f <- t(xi.hat) %*% PZ %*% xi.hat;
print("Updated GMM objective:")
print(f <- as.numeric(f));
return(f)
}
jacobian <- function(delta.in, theta.in){
print(paste0("Calculating Jacobian matrix, ", Sys.time()))
#Requires global variables X, v, mkt.id
mu1 <- X %*% diag(theta.in) %*% v;
ind.shares <- ind_sh(delta.in, mu1);
K <- ncol(X);
print(paste0("Calculating dsigma matrix, ", Sys.time()))
dsigma <- lapply(l.Xv, function(x){
temp2 <- x * ind.shares;
temp3 <- as.matrix(do.call("rbind", lapply(mkt.id, function(m){
colSums(temp2[mkt.id %in% m, ])
})));
dsigma.res <- rowMeans(temp2 - ind.shares * temp3);
return(dsigma.res)
})
dsigma <- as.matrix(do.call("cbind", dsigma))
print(paste0("Calculating ddelta matrices, ", Sys.time()))
ddelta <- list()
for(m in mkt.id){
if(m %in% names(ddelta)){next}
temp1 <- as.matrix(ind.shares[mkt.id %in% m, ]);
H1 <- temp1 %*% t(temp1);
H2 <- diag(rowSums(temp1));
H <- (H2 - H1) / n.sim;
H.inv <- solve(H);
ddelta[[as.character(m)]] <- H.inv %*% dsigma[mkt.id %in% m, ];
rm(temp1, H1, H2, H, H.inv)
}
ddelta <- as.matrix(do.call("rbind", ddelta));
return(ddelta)
}
gradient_obj <- function(theta2){
#Requires global variables PZ, delta, xi.hat
print(system.time(jacobian_res <<- jacobian(as.vector(dat[, "delta"]), theta2)))
print(paste0("Updated gradient:", Sys.time()))
print(f <- -2 * as.numeric(t(jacobian_res) %*% PZ %*% xi.hat));
#######
L <- ncol(Z)
covg <- matrix(0, nrow = L, ncol = L)
for(i in 1:JT){
covg <- covg + (Z[i, ] %*% t(Z[i, ])) * xi.hat[i]^2
}
d.delta <- jacobian_res;
Dg <- t(d.delta) %*% Z
p.Dg <- try(solve(Dg %*% W.inv %*% t(Dg)))
cov.mat <- p.Dg %*% (Dg %*% W.inv %*% covg %*% W.inv %*% t(Dg)) %*% p.Dg
beta.est$sigma.se <<- sqrt(diag(cov.mat));
print(paste0("Updated coefficients table:", Sys.time()))
print(beta.est)
write.csv(beta.est, file = paste0("BLP_beta_est_", Sys.Date(), ".csv"))
#######
return(as.numeric(f))
}
#Set up data
dat <- dat[dat[, share.fld] > 0, ]
dat <- dat[order(dat[, mkt.id.fld], dat[, prod.id.fld]), ]
JT <- nrow(dat)
#market identifier variable
mkt.id <- dat[, mkt.id.fld];
#Number of characteristics (including constant and price)
X <- as.matrix(cbind(ones = rep(1, JT), dat[, c(x.var.flds, prc.fld)]));
K <- ncol(X)
#Compute the outside good market share by market
s.jt <- as.vector(dat[, share.fld]);
temp <- aggregate(s.jt, by = list(mkt.id = mkt.id), sum);
sum1 <- temp$x[match(mkt.id, temp$mkt.id)];
s.j0 <- as.vector(1 - sum1);
rm(temp, sum1);
dat[, "delta"] <- Y <- log(s.jt) - log(s.j0);
iv <- dat[, prc.iv.flds]
while(!require(AER)){install.packages("AER")}
#Construct 2SLS regression specification
str.ivreg.y <- "delta ~ "
str.ivreg.x <- paste(x.var.flds, collapse = " + ")
str.ivreg.prc <- paste(prc.fld, collapse = " + ")
str.ivreg.iv <- paste(prc.iv.flds, collapse = " + ")
print("2SLS specification:")
print(fm.ivreg <- paste0(str.ivreg.y, str.ivreg.x, " + ", str.ivreg.prc, " | ", str.ivreg.x, " + ", str.ivreg.iv))
rm(str.ivreg.y, str.ivreg.x, str.ivreg.prc, str.ivreg.iv)
print("2SLS beta estimate:")
print(summary(mo.ivreg <- ivreg(fm.ivreg, data = dat, x = TRUE)))
beta.est <- summary(mo.ivreg)$coef[, 1:2]
#Z = instrumental variable matrix include exogenous X's
Z <- as.matrix(mo.ivreg$x$instruments)
PZ <- Z %*% solve(t(Z) %*% Z) %*% t(Z);
theta1 <- coef(mo.ivreg);
xi.hat <- as.vector(mo.ivreg$resid);
Z.hat <- Z * matrix(rep(xi.hat, ncol(Z)), ncol = ncol(Z))
W.inv <- try(solve(t(Z.hat) %*% Z.hat), silent = FALSE)
if("matrix" == class(W.inv)){
PZ <- Z %*% W.inv %*% t(Z);
PX.inv <- solve(t(X) %*% PZ %*% X)
theta1 <- PX.inv %*% t(X) %*% PZ %*% Y
xi.hat <- Y - X %*% theta1
X.hat <- (PZ %*% X) * matrix(rep(xi.hat, K), ncol = K)
tsls.se <- sqrt(diag(PX.inv %*% t(X.hat) %*% X.hat %*% PX.inv))
print("GMM step 2 updated theta1 estimate:")
print(beta.est <- data.frame(beta.est = theta1, se.est = tsls.se))
}
dat[, "xi.hat"] <- xi.hat
#Starting point
print("Sigma guess:")
if(missing(sigma.guess)){
tsls.se <- beta.est[, 2]
print(theta2 <- 0.5 * tsls.se);
} else {
print(theta2 <- sigma.guess);
}
theta2.prev <- theta2;
# Matrix of individuals' characteristics
#	Standard normal distribution draws, one for each characteristic
v <- matrix(rnorm(K * n.sim), nrow = K, ncol = n.sim)
# Break X and v matrices into list variables
# in attempt to expedite calculation of the Jacobian matrix
l.X <- lapply(1:K, function(k){
return(X[, k])
})
l.v <- lapply(1:K, function(k){
return(v[k, ])
})
l.Xv <- lapply(1:K, function(k){
l.X[[k]] %*% t(l.v[[k]]);
})
print("Estimating random coefficients multinomial logit")
a <- 0;
beta.est <- NULL;
while(!require(SQUAREM)){install.packages("SQUAREM")}
while(!require(BB)){install.packages("BB")}
print(system.time(
theta.est <- multiStart(par = theta2, fn = gmm_obj, gr = gradient_obj, lower = 0, control = list(trace = TRUE, checkGrad=FALSE), action = "optimize")
));
save(theta.est, file = paste0("theta_est_", Sys.time(), ".RData"))
print("Final coefficients estimate:")
theta2 <- theta.est$par
theta2 <- as.numeric(theta2)
gmm.res <- gmm_obj(theta2)
grad.res <- gradient_obj(theta2)
return(list(coef.mat = beta.est, gmm.obj.func = gmm.res, gmm_est = theta.est, final.data = dat))
}
load("~/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/theta_est_2018-02-24 16:03:28.RData")
View(theta.est)
load("~/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/theta_est_2018-02-24 16:03:28.RData")
load("~/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/theta_est_2018-02-24 16:03:28.RData")
View(theta.est)
load("~/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/theta_est_2018-02-25 14:37:42.RData")
oldt2 <- readRDS("~/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/oldt2.Rds")
View(oldt2)
rm(list=ls())
library(hdm)
data(BLP)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
rm(BLP)
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results1 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("hpwt", "air", "mpg", "space", "trend"), n.sim=200)
View(results1)
results1[["coef.mat"]][["beta.est"]]
rm(list=ls())
library(hdm)
data(BLP)
dat <- BLP$BLP
View(dat)
rm(list=ls())
library(hdm)
dat <- data(BLP)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
rm(BLP)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
rm(BLP)
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
BerryLevinsohnPakes()
BerryLevinsohnPakes
install.packages("pillar", lib="/Library/Frameworks/R.framework/Versions/3.4/Resources/library")
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results1 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("hpwt", "air", "mpg", "space", "trend"), n.sim=200)
results2 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("hpwt", "air", "mpg", "space", "trend"), n.sim=200)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
set.seed(1234)
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results_term1 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("hpwt", "air", "mpg", "space", "trend"), n.sim=200)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
set.seed(1234)
results_seed1 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results_seed1 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
results_seed2 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
load("~/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/theta_est_2018-02-26 21:47:53.RData")
View(theta.est)
View(results_seed1)
results_seed2 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200, sigma.guess=c(0,0,0,0,0))
results_seed2 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200, sigma.guess=c(0.1,0.1,0.1,0.1,0.1))
results_seed2 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200, sigma.guess=c(1,0.1,1,0.1,1))
results_seed2 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200, sigma.guess=c(1,1,1,1,1))
results_seed2 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200, sigma.guess=c(10,100,10,100,10))
results_seed2 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200, sigma.guess=c(10,1,.10,1.100,10))
results_seed2 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results_seed2)
View(results_seed1)
View(results_seed2)
View(results_seed1)
View(results_seed2)
results_seed3 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results_seed3)
View(results_seed2)
View(results_seed1)
results_seed3[["gmm_est"]][["par"]]
install.packages(c("pillar", "plotmo", "RMySQL", "tidyselect"), lib="/Library/Frameworks/R.framework/Versions/3.4/Resources/library")
install.packages("RMySQL", lib="/Library/Frameworks/R.framework/Versions/3.4/Resources/library")
install.packages(c("lubridate", "truncnorm"))
install.packages("lubridate")
install.packages("truncnorm")
install.packages("httpuv", lib="/Library/Frameworks/R.framework/Versions/3.4/Resources/library")
install.packages("yaml", lib="/Library/Frameworks/R.framework/Versions/3.4/Resources/library")
install.packages("RMySQL", lib="/Library/Frameworks/R.framework/Versions/3.4/Resources/library")
?halton
??halton
library(randtoolbox)
a <- halton(100)
plot(a)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results_seed3 <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results_seed3)
install.packages("httpuv", lib="/Library/Frameworks/R.framework/Versions/3.4/Resources/library")
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results)
print(paste0("GMM Loop number: ", Sys.time()))
print(a <<- a + 1)
a <- 0;
print(a <<- a + 1)
print("Updated theta2 estimate:")
print(paste0("GMM Loop number: ", Sys.time()))
print(a <<- a + 1)
# This function computes the GMM objective function
# Requires global variable inputs: X, v, delta, a, W
# Outputs: theta1, xi.hat
print(paste0("GMM Loop number: ", Sys.time()))
print(a <<- a + 1)
View(results)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)
View(results)
rm(list=ls())
library(hdm)
dat <- BLP$BLP
dat$price <- dat$price + 11.761
source("/Users/malooney/Google Drive/digitalLibrary/*BLP_Algos/BLP_Algos/BLP_Orig_function.R")
results <- BerryLevinsohnPakes(dat=dat, mkt.id.fld = "cdid", prod.id.fld = "id", prc.fld = "price", share.fld="share", x.var.flds = c("hpwt", "air", "mpd", "space"), prc.iv.flds = c("mpg", "trend"), n.sim=200)