


# mFilter

The mFilter package implements several time series filters useful for
smoothing and extracting trend and cyclical components of a time series.
The routines are commonly used in economics and finance, however they
should also be interest to other areas. Currently,
Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and
trigonometric regression filters are included in the package.

## Installation

You can install the released version of mFilter from
[CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("mFilter")
```

The development version can be installed with:

``` r
devtools::install_github("mbalcilar/mFilter")
```

## Example

This is a basic example which shows you how to do Butterworth filtering:

``` r
library(mFilter)
data(unemp)
unemp.bw <- bwfilter(unemp)
plot(unemp.bw)
```


``` r
unemp.bw1 <- bwfilter(unemp, drift=TRUE)
unemp.bw2 <- bwfilter(unemp, freq=8,drift=TRUE)
unemp.bw3 <- bwfilter(unemp, freq=10, nfix=3, drift=TRUE)
unemp.bw4 <- bwfilter(unemp, freq=10, nfix=4, drift=TRUE)
```

``` r
par(mfrow=c(2,1),mar=c(3,3,2,1),cex=.8)
plot(unemp.bw1$x,
     main="Butterworth filter of unemployment: Trend, 
     drift=TRUE",col=1, ylab="")
lines(unemp.bw1$trend,col=2)
lines(unemp.bw2$trend,col=3)
lines(unemp.bw3$trend,col=4)
lines(unemp.bw4$trend,col=5)
legend("topleft",legend=c("series", "freq=10, nfix=2", 
       "freq=8, nfix=2", "freq=10, nfix=3", "freq=10, nfix=4"), 
       col=1:5, lty=rep(1,5), ncol=1)
```


``` r
plot(unemp.bw1$cycle,
     main="Butterworth filter of unemployment: Cycle,drift=TRUE", 
     col=2, ylab="", ylim=range(unemp.bw3$cycle,na.rm=TRUE))
lines(unemp.bw2$cycle,col=3)
lines(unemp.bw3$cycle,col=4)
lines(unemp.bw4$cycle,col=5)
```


