R: Indirect Utility Function of the Almost Ideal Demand System
aidsUtility
R Documentation
Indirect Utility Function of the Almost Ideal Demand System
Description
These functions calculate the utility level
given prices and total expenditure
using the indirect utility function
of the Almost Ideal Demand System
and the partial derivatives of this indirect utility function
with repect to prices and total expenditure.
a vector of strings containing the names of the prices.
totExpName
a string containing the variable name of total expenditure.
coef
a list containing the coefficients in elements
alpha0 (scalar), alpha (vector), beta (vector), gamma (matrix),
and possibly beta0 (scalar, if not given, it is assumed to be 1).
data
a data frame containing the data.
rel
logical. If TRUE the returned partial derivatives are
given in relative terms (like elasticities),
i.e. they indicate the percentage change in the utility level
when a price or total expenditure is increased by 1%.
Value
aidsUtility returns a numeric vector
that contains the utility levels;
aidsUtilityDeriv returns a data.frame
that contains the partial derivatives
of the indirect utility function
with repect to prices and total expenditure.
Author(s)
Arne Henningsen
References
Deaton, A.S. and J. Muellbauer (1980)
An Almost Ideal Demand System.
American Economic Review, 70, p. 312-326.
See Also
aidsEst, aidsCalc
Examples
data( Blanciforti86 )
# Data on food consumption are available only for the first 32 years
Blanciforti86 <- Blanciforti86[ 1:32, ]
priceNames <- c( "pFood1", "pFood2", "pFood3", "pFood4" )
shareNames <- c( "wFood1", "wFood2", "wFood3", "wFood4" )
## estimate the (non-linear) AIDS
estResult <- aidsEst( priceNames, shareNames, "xFood",
data = Blanciforti86, method = "IL" )
# calculate the utility levels of each year
utility <- aidsUtility( priceNames, "xFood", coef = coef( estResult ),
data = Blanciforti86 )
utilityDeriv <- aidsUtilityDeriv( priceNames, "xFood",
coef = coef( estResult ), data = Blanciforti86 )
utilityEla <- aidsUtilityDeriv( priceNames, "xFood",
coef = coef( estResult ), data = Blanciforti86, rel = TRUE )