# bkfilter: Baxter-King filter of a time series In mbalcilar/mFilter: Miscellaneous Time Series Filters

## 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

 1 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

## 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 ≤ p_l < p_u < ∞.

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 (-π, π). a and b are related to p_l and p_u by

a=\frac{2 π}{p_u}\ \ \ \ \ {b=\frac{2 π}{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) = ∑^∞_{j=-∞} 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)}{π j}

B_0=\frac{b-a}{π}

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=∑^{n}_{j=-n}\hat{B}_{j} x_{t+j}=\hat{B}_0 x_t + ∑^{n}_{j=1} \hat{B}_j (x_{t-j}+x_{t+j})

where

\hat{B}_j=B_j-\frac{1}{2n+1}∑^{n}_{j=-n}B_{j}

If drift=TRUE the drift adjusted series is obtained

\tilde{x}_{t}=x_t-t≤ft(\frac{x_{T}-x_{1}}{T-1}\right), \ \ t=0,1,…,T-1

where \tilde{x}_{t} is the undrifted series.

## Value

A "mFilter" object (see mFilter).

## Author(s)

Mehmet Balcilar, [email protected]

## 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.

mFilter, bwfilter, cffilter, hpfilter, trfilter
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 ## 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)