censoredElasticity: Numerical approximation to censored AI or QUAI demand system...

In censoredAIDS: Estimation of Censored AI/QUAI Demand System via Maximum Likelihood Estimation (MLE)

Numerical approximation to censored AI or QUAI demand system elasticities, including demographic variable.

Description

Numerical approximation to censored AI or QUAI demand system elasticities, including demographic variable.

Usage

censoredElasticity(
  Prices = matrix(),
  Budget = matrix(),
  ShareNames = NULL,
  Demographics = matrix(),
  DemographicNames = NULL,
  Params = matrix(),
  quaids = FALSE,
  vcov = matrix(),
  func,
  ...
)

Arguments

Prices	A matrix of logged prices with (nxm) dimensions where n is the number of observations and m the number of shares.
Budget	A matrix of logged total expenditure/budget with (nx1) dimensions where n is the number of observations.
ShareNames	A vector of strings containing the share names with (mx1) dimensions where m is the number of shares.
Demographics	A matrix of demographic variables with (nxt) dimensions where n is the number of observations and t the number of demographic variables.
DemographicNames	A vector of strings containing the demographic names with (tx1) dimensions where t is the number of demographic variables.
Params	A vector containing the parameters alpha, beta, gamma, and theta and lambda if elected.
quaids	Logical. Should quadratic form be used instead?
vcov	A variance-covariance matrix of the parameters. Must be positive semi-definite and symmetric.
func	A function to be applied to the data at which point estimate elasticities are being evaluated.
...	Additional arguments to be passed to func.

Value

A list containing a matrix of price and income elasticities, a matrix with their respective standard errors approximated using the delta method, and a matrix of expected shares that can serve to run further post-estimation analyses.

Examples

## Not run: 

testing_data <- censoredAIDS::MexicanHH_foodConsumption

# Organizing the data for comfort
s1 <- testing_data$s1
s2 <- testing_data$s2
s3 <- testing_data$s3
s4 <- testing_data$s4
s5 <- testing_data$s5
s6 <- testing_data$s6

lnp1 <- testing_data$lnp1
lnp2 <- testing_data$lnp2
lnp3 <- testing_data$lnp3
lnp4 <- testing_data$lnp4
lnp5 <- testing_data$lnp5
lnp6 <- testing_data$lnp6

age <- testing_data$age
size <- testing_data$size
sex <- testing_data$sex
educ <- testing_data$educ

# Alpha
b0 <- rep(0, 5)

# Beta
b0 <- c(b0, rep(0.003, 5))

# Gamma
b0 <- c(b0,0.01,0,0.01,0,0, 0.01,0,0,0,0.01,0,0,0,0,0.01)

# Demos
b0 <- c(b0,rep(0.002, 20))

# Sigma
b0 <- c(b0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,1)

vcov <- matrix(0, nrow = length(b0), ncol = length(b0))
vcov[upper.tri(vcov, diag = TRUE)] <- runif(.5*length(b0)*(1+length(b0)))
vcov <- t(vcov) %*% vcov

list_etas <- censoredElasticity(
Params = b0,
Shares = matrix(c(s1, s2, s3, s4, s5, s6), ncol = 6),
Prices = matrix(c(lnp1, lnp2, lnp3, lnp4, lnp5, lnp6), ncol = 6),
Budget = matrix(testing_data$lnw),
Demographics = matrix(c(age, size, educ, sex), ncol = 4),
quaids = FALSE,
func = mean,
na.rm = TRUE,
vcov = vcov
)

## End(Not run)

censoredAIDS documentation built on April 12, 2025, 1:53 a.m.