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\name{FrozenJuice}
\alias{FrozenJuice}
\title{Price of Frozen Orange Juice}
\description{
Monthly data on the price of frozen orange juice concentrate and temperature
in the orange-growing region of Florida.
}
\usage{data("FrozenJuice")}
\format{
A monthly multiple time series from 1950(1) to 2000(12) with 3 variables.
\describe{
\item{price}{Average producer price for frozen orange juice.}
\item{ppi}{Producer price index for finished goods. Used to deflate the
overall producer price index for finished goods to eliminate the
effects of overall price inflation.}
\item{fdd}{Number of freezing degree days at the Orlando, Florida, airport.
Calculated as the sum of the number of degrees Fahrenheit that the
minimum temperature falls below freezing (32 degrees Fahrenheit = about 0 degrees Celsius)
in a given day over all days in the month: \code{fdd} = sum(max(0, 32 - minimum daily temperature)),
e.g. for February \code{fdd} is the number of freezing degree days from January 11
to February 10.}
}
}
\details{
The orange juice price data are the frozen orange juice component of processed foods and
feeds group of the Producer Price Index (PPI), collected by the US Bureau of Labor
Statistics (BLS series wpu02420301). The orange juice price series was divided by the
overall PPI for finished goods to adjust for general price inflation. The freezing degree
days series was constructed from daily minimum temperatures recorded at Orlando area
airports, obtained from the National Oceanic and Atmospheric Administration (NOAA)
of the US Department of Commerce.
}
\source{
Online complements to Stock and Watson (2007).
}
\references{
Stock, J.H. and Watson, M.W. (2007). \emph{Introduction to Econometrics}, 2nd ed. Boston: Addison Wesley.
}
\seealso{\code{\link{StockWatson2007}}}
\examples{
## load data
data("FrozenJuice")
## Stock and Watson, p. 594
library("dynlm")
fm_dyn <- dynlm(d(100 * log(price/ppi)) ~ fdd, data = FrozenJuice)
coeftest(fm_dyn, vcov = vcovHC(fm_dyn, type = "HC1"))
## equivalently, returns can be computed 'by hand'
## (reducing the complexity of the formula notation)
fj <- ts.union(fdd = FrozenJuice[, "fdd"],
ret = 100 * diff(log(FrozenJuice[,"price"]/FrozenJuice[,"ppi"])))
fm_dyn <- dynlm(ret ~ fdd, data = fj)
## Stock and Watson, p. 595
fm_dl <- dynlm(ret ~ L(fdd, 0:6), data = fj)
coeftest(fm_dl, vcov = vcovHC(fm_dl, type = "HC1"))
## Stock and Watson, Table 15.1, p. 620, numbers refer to columns
## (1) Dynamic Multipliers
fm1 <- dynlm(ret ~ L(fdd, 0:18), data = fj)
coeftest(fm1, vcov = NeweyWest(fm1, lag = 7, prewhite = FALSE))
## (2) Cumulative Multipliers
fm2 <- dynlm(ret ~ L(d(fdd), 0:17) + L(fdd, 18), data = fj)
coeftest(fm2, vcov = NeweyWest(fm2, lag = 7, prewhite = FALSE))
## (3) Cumulative Multipliers, more lags in NW
coeftest(fm2, vcov = NeweyWest(fm2, lag = 14, prewhite = FALSE))
## (4) Cumulative Multipliers with monthly indicators
fm4 <- dynlm(ret ~ L(d(fdd), 0:17) + L(fdd, 18) + season(fdd), data = fj)
coeftest(fm4, vcov = NeweyWest(fm4, lag = 7, prewhite = FALSE))
## monthly indicators needed?
fm4r <- update(fm4, . ~ . - season(fdd))
waldtest(fm4, fm4r, vcov= NeweyWest(fm4, lag = 7, prewhite = FALSE)) ## close ...
}
\keyword{datasets}
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