decomp {timsac}R Documentation

Time Series Decomposition (Seasonal Adjustment) by Square-Root Filter

Description

Decompose a nonstationary time series into several possible components by square-root filter.

Usage

  decomp(y, trend.order = 2, ar.order = 2, frequency = 12,
         seasonal.order = 1, log = FALSE, trade = FALSE, diff = 1,
         year = 1980, month = 1, miss = 0, omax = 99999.9, plot = TRUE)

Arguments

y

a univariate time series.

trend.order

trend order (0, 1, 2 or 3).

ar.order

AR order (less than 11, try 2 first).

frequency

number of seasons in one period.

seasonal.order

seasonal order (0, 1 or 2).

log

log transformation of data (if log = TRUE).

trade

trading day adjustment (if trade = TRUE).

diff

numerical differencing (1 sided or 2 sided).

year

the first year of the data.

month

the first month of the data.

miss

missing data flag.

= 0 : no consideration
> 0 : values which are greater than omax are treated as missing data
< 0 : values which are less than omax are treated as missing data
omax

maximum or minimum data value (if miss > 0 or miss < 0).

plot

logical. If TRUE (default), trend, seasonal, ar and trad are plotted.

Details

The Basic Model

y(t) = T(t) + AR(t) + S(t) + TD(t) + W(t)

where T(t) is trend component, AR(t) is AR process, S(t) is seasonal component, TD(t) is trading day factor and W(t) is observational noise.

Component Models

Value

trend

trend component.

seasonal

seasonal component.

ar

AR process.

trad

trading day factor.

noise

observational noise.

aic

AIC.

lkhd

likelihood.

sigma2

sigma^2.

tau1

system noise variances tau2(1).

tau2

system noise variances tau2(2).

tau3

system noise variances tau2(3).

arcoef

vector of AR coefficients.

tdf

trading day factor. tdf(i) (i=1,7) are from Sunday to Saturday sequentially.

References

G.Kitagawa (1981) A Nonstationary Time Series Model and Its Fitting by a Recursive Filter Journal of Time Series Analysis, Vol.2, 103-116.

W.Gersch and G.Kitagawa (1983) The prediction of time series with Trends and Seasonalities Journal of Business and Economic Statistics, Vol.1, 253-264.

G.Kitagawa (1984) A smoothness priors-state space modeling of Time Series with Trend and Seasonality Journal of American Statistical Association, VOL.79, NO.386, 378-389.

Examples

data(Blsallfood)
z <- decomp(Blsallfood, trade = TRUE, year = 1973)
z$aic
z$lkhd
z$sigma2
z$tau1
z$tau2
z$tau3

[Package timsac version 1.3.6 Index]