mFilter (version 0.1-5)

bkfilter: Baxter-King filter of a time series

Description

This function implements the Baxter-King approximation to the band pass filter for a time series. The function computes cyclical and trend components of the time series using band-pass approximation for fixed and variable length filters.

Usage

bkfilter(x,pl=NULL,pu=NULL,nfix=NULL,type=c("fixed","variable"),drift=FALSE)

Arguments

x

a regular time series

type

character, indicating the filter type, "fixed", for the fixed length Baxter-King filter (default), "variable", for the variable length Baxter-King filter.

pl

integer. minimum period of oscillation of desired component (pl<=2).

pu

integer. maximum period of oscillation of desired component (2<=pl<pu<infinity).

drift

logical, FALSE if no drift in time series (default), TRUE if drift in time series.

nfix

sets fixed lead/lag length or order of the filter. The nfix option sets the order of the filter by 2*nfix+1. The default is frequency(x)*3.

Value

A "mFilter" object (see mFilter).

Details

Almost all filters in this package can be put into the following framework. Given a time series \(\{x_t\}^T_{t=1}\) we are interested in isolating component of \(x_t\), denoted \(y_t\) with period of oscillations between \(p_l\) and \(p_u\), where \(2 \le p_l < p_u < \infty\).

Consider the following decomposition of the time series $$x_t = y_t + \bar{x}_t$$ The component \(y_t\) is assumed to have power only in the frequencies in the interval \(\{(a,b) \cup (-a,-b)\} \in (-\pi, \pi)\). \(a\) and \(b\) are related to \(p_l\) and \(p_u\) by $$a=\frac{2 \pi}{p_u}\ \ \ \ \ {b=\frac{2 \pi}{p_l}}$$

If infinite amount of data is available, then we can use the ideal bandpass filter $$y_t = B(L)x_t$$ where the filter, \(B(L)\), is given in terms of the lag operator \(L\) and defined as $$B(L) = \sum^\infty_{j=-\infty} B_j L^j, \ \ \ L^k x_t = x_{t-k}$$ The ideal bandpass filter weights are given by $$B_j = \frac{\sin(jb)-\sin(ja)}{\pi j}$$ $$B_0=\frac{b-a}{\pi}$$

The Baxter-King filter is a finite data approximation to the ideal bandpass filter with following moving average weights $$y_t = \hat{B}(L)x_t=\sum^{n}_{j=-n}\hat{B}_{j} x_{t+j}=\hat{B}_0 x_t + \sum^{n}_{j=1} \hat{B}_j (x_{t-j}+x_{t+j})$$ where $$\hat{B}_j=B_j-\frac{1}{2n+1}\sum^{n}_{j=-n}B_{j}$$

If drift=TRUE the drift adjusted series is obtained $$\tilde{x}_{t}=x_t-t\left(\frac{x_{T}-x_{1}}{T-1}\right), \ \ t=0,1,\dots,T-1$$ where \(\tilde{x}_{t}\) is the undrifted series.

References

M. Baxter and R.G. King. Measuring business cycles: Approximate bandpass filters. The Review of Economics and Statistics, 81(4):575-93, 1999.

L. Christiano and T.J. Fitzgerald. The bandpass filter. International Economic Review, 44(2):435-65, 2003.

J. D. Hamilton. Time series analysis. Princeton, 1994.

R.J. Hodrick and E.C. Prescott. Postwar US business cycles: an empirical investigation. Journal of Money, Credit, and Banking, 29(1):1-16, 1997.

R.G. King and S.T. Rebelo. Low frequency filtering and real business cycles. Journal of Economic Dynamics and Control, 17(1-2):207-31, 1993.

D.S.G. Pollock. Trend estimation and de-trending via rational square-wave filters. Journal of Econometrics, 99:317-334, 2000.

See Also

mFilter, bwfilter, cffilter, hpfilter, trfilter

Examples

# NOT RUN {
## library(mFilter)

data(unemp)

opar <- par(no.readonly=TRUE)

unemp.bk <- bkfilter(unemp)
plot(unemp.bk)
unemp.bk1 <- bkfilter(unemp, drift=TRUE)
unemp.bk2 <- bkfilter(unemp, pl=8,pu=40,drift=TRUE)
unemp.bk3 <- bkfilter(unemp, pl=2,pu=60,drift=TRUE)
unemp.bk4 <- bkfilter(unemp, pl=2,pu=40,drift=TRUE)

par(mfrow=c(2,1),mar=c(3,3,2,1),cex=.8)
plot(unemp.bk1$x,
    main="Baxter-King filter of unemployment: Trend, drift=TRUE",
    col=1, ylab="")
lines(unemp.bk1$trend,col=2)
lines(unemp.bk2$trend,col=3)
lines(unemp.bk3$trend,col=4)
lines(unemp.bk4$trend,col=5)
legend("topleft",legend=c("series", "pl=2, pu=32", "pl=8, pu=40",
      "pl=2, pu=60", "pl=2, pu=40"), col=1:5, lty=rep(1,5), ncol=1)

plot(unemp.bk1$cycle,
main="Baxter-King filter of unemployment: Cycle,drift=TRUE",
      col=2, ylab="", ylim=range(unemp.bk3$cycle,na.rm=TRUE))
lines(unemp.bk2$cycle,col=3)
lines(unemp.bk3$cycle,col=4)
lines(unemp.bk4$cycle,col=5)
## legend("topleft",legend=c("pl=2, pu=32", "pl=8, pu=40", "pl=2, pu=60",
## "pl=2, pu=40"), col=1:5, lty=rep(1,5), ncol=1)

par(opar)
# }