Dutch Retail Sales Index Data

Time series of retail sales index in The Netherlands.

Usage

data("DutchSales")

Format

A monthly univariate time series from 1960(5) to 1995(9).

Source

Online complements to Franses (1998).

References

Franses, P.H. (1998). Time Series Models for Business and Economic Forecasting. Cambridge, UK: Cambridge University Press.

See also

Franses1998

Examples

data("DutchSales")
plot(DutchSales)


## EACF tables (Franses 1998, p. 99)
ctrafo <- function(x) residuals(lm(x ~ factor(cycle(x))))
ddiff <- function(x) diff(diff(x, frequency(x)), 1)
eacf <- function(y, lag = 12) {
  stopifnot(all(lag > 0))
  if(length(lag) < 2) lag <- 1:lag
  rval <- sapply(
    list(y = y, dy = diff(y), cdy = ctrafo(diff(y)),
         Dy = diff(y, frequency(y)), dDy = ddiff(y)),
    function(x) acf(x, plot = FALSE, lag.max = max(lag))$acf[lag + 1])
  rownames(rval) <- lag
  return(rval)
}

## Franses (1998), Table 5.3
round(eacf(log(DutchSales), lag = c(1:18, 24, 36)), digits = 3)
#>        y     dy    cdy    Dy    dDy
#> 1  0.980 -0.264 -0.556 0.456 -0.532
#> 2  0.967 -0.238 -0.024 0.490 -0.121
#> 3  0.961 -0.004  0.221 0.654  0.307
#> 4  0.954 -0.256 -0.180 0.486 -0.200
#> 5  0.954  0.163  0.010 0.534 -0.011
#> 6  0.950  0.236  0.160 0.593  0.148
#> 7  0.940  0.093 -0.150 0.492 -0.093
#> 8  0.929 -0.195 -0.025 0.492 -0.106
#> 9  0.922 -0.004  0.223 0.607  0.268
#> 10 0.912 -0.306 -0.256 0.431 -0.276
#> 11 0.913 -0.098 -0.035 0.556  0.228
#> 12 0.916  0.816  0.453 0.432 -0.061
#> 13 0.897 -0.248 -0.497 0.375 -0.290
#> 14 0.885 -0.113  0.344 0.633  0.408
#> 15 0.877 -0.112 -0.125 0.446 -0.119
#> 16 0.870 -0.238 -0.109 0.392 -0.189
#> 17 0.870  0.218  0.176 0.540  0.240
#> 18 0.865  0.181 -0.008 0.429 -0.045
#> 24 0.827  0.656 -0.007 0.300 -0.308
#> 36 0.738  0.593 -0.125 0.210 -0.312