FrozenJuice {AER} | R Documentation

## 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.

price

  Average producer price for frozen orange juice.

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.

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: `fdd` = sum(max(0, 32 - minimum daily temperature)), e.g. for February `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). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

### See Also

`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 ...

[Package AER version 1.2-6 Index]