CigarettesSW: Cigarette Consumption Panel Data

In gmm4: S4 Generalized Method of Moments

Description

Panel data on cigarette consumption for the 48 continental US States from 1985–1995.

Usage

data("CigarettesSW")

Format

A data frame containing 48 observations on 7 variables for 2 periods.

state

Factor indicating state.

year

Factor indicating year.

cpi

Consumer price index.

population

State population.

packs

Number of packs per capita.

income

State personal income (total, nominal).

tax

Average state, federal and average local excise taxes for fiscal year.

price

Average price during fiscal year, including sales tax.

taxs

Average excise taxes for fiscal year, including sales tax.

Source

Online complements to Stock and Watson (2007). The dataset and this help file comes from the AER package.

References

Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

Christian Kleiber and Achim Zeileis (2008). Applied Econometrics with R. New York: Springer-Verlag. ISBN 978-0-387-77316-2. URL https://CRAN.R-project.org/package=AER

Examples

## Stock and Watson (2007)
## data and transformations
data(CigarettesSW)
CigarettesSW$rprice <- with(CigarettesSW, price/cpi)
CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)
CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi)
c1985 <- subset(CigarettesSW, year == "1985")
c1995 <- subset(CigarettesSW, year == "1995")

## Equation 12.15
model1 <- gmmModel(log(packs)~log(rprice)+log(rincome),
                   ~log(rincome)+tdiff, data = c1995, vcov="MDS")
res1 <- modelFit(model1)

## HC0 robust se (different from the textbook)
summary(res1, sandwich=TRUE)

## HC1 robust se (like in the textbook)
## A little harder to get, but is it really worth it
## in the case of GMM?

summary(res1, sandwich=TRUE, df.adj=TRUE)@coef

## Equation 12.16
model2<- gmmModel(log(packs)~log(rprice)+log(rincome),
                  ~log(rincome)+tdiff+I(tax/cpi), data = c1995,
                  centeredVcov=FALSE, vcov="MDS")
res2<- tsls(model2)
summary(res2, sandwich=TRUE, df.adj=TRUE)

## Table 12.1
data <- data.frame(dQ=log(c1995$pack/c1985$pack),
                   dP=log(c1995$rprice/c1985$rprice),
                   dTs=c1995$tdiff-c1985$tdiff,
                   dT=c1995$tax/c1995$cpi-c1985$tax/c1985$cpi,
                   dInc=log(c1995$rincome/c1985$rincome))
model1 <- gmmModel(dQ~dP+dInc, ~dInc+dTs, vcov="MDS", data=data)
model2 <- gmmModel(dQ~dP+dInc, ~dInc+dT, vcov="MDS", data=data)
model3 <- gmmModel(dQ~dP+dInc, ~dInc+dTs+dT, vcov="MDS", data=data)

res1 <- tsls(model1)
summary(res1, TRUE, TRUE)
res2 <- tsls(model2)
summary(res2, TRUE, TRUE)
res3 <- tsls(model3)
summary(res3, TRUE, TRUE)

gmm4 documentation built on Dec. 6, 2019, 3:01 a.m.