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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# mFilter
The mFilter package implements several time series filters useful
for smoothing and extracting trend and cyclical components of a
time series. The routines are commonly used in economics and
finance, however they should also be interest to other areas.
Currently, Christiano-Fitzgerald, Baxter-King,
Hodrick-Prescott, Butterworth, and trigonometric regression
filters are included in the package.
## Installation
You can install the released version of mFilter from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("mFilter")
```
The development version can be installed with:
``` r
devtools::install_github("mbalcilar/mFilter")
```
## Example
This is a basic example which shows you how to do Butterworth filtering:
```{r ex1}
library(mFilter)
data(unemp)
unemp.bw <- bwfilter(unemp)
plot(unemp.bw)
unemp.bw1 <- bwfilter(unemp, drift=TRUE)
unemp.bw2 <- bwfilter(unemp, freq=8,drift=TRUE)
unemp.bw3 <- bwfilter(unemp, freq=10, nfix=3, drift=TRUE)
unemp.bw4 <- bwfilter(unemp, freq=10, nfix=4, drift=TRUE)
```
```{r ex2}
par(mfrow=c(2,1),mar=c(3,3,2,1),cex=.8)
plot(unemp.bw1$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 ex3}
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)
```