---
title: "cointmonitoR"
author: "Philipp Aschersleben"
date: "`r Sys.Date()`"
output:
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 3
vignette: >
  %\VignetteIndexEntry{cointmonitoR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
```{R, include = FALSE}
knitr::opts_chunk$set(fig.path = "figures/vignette-", fig.width = 5, message = FALSE)
library(cointmonitoR)
```


## Install cointmonitoR

When installing the current CRAN version of `cointmonitoR`, the latest version of our `cointReg` package will also be installed automatically. It provides functions for parameter estimation and inference in cointegrating regressions and the `cointmonitoR` package depends on it. See its [CRAN](https://cran.r-project.org/package=cointReg) and [GitHub](https://github.com/aschersleben/cointReg) pages for further information.

```{R, eval = FALSE}
install.packages("cointmonitoR")
library("cointmonitoR")
```


## Basic examples

### Monitoring Stationarity

Generate a time series to monitor. Since it's the easiest way to generate a stationary time series, we get `x1` as a vector of random number from a standard normal distribution.
```{R}
set.seed(1909)
x1 <- rnorm(200)
```

The use of the monitoring procedure is really simple, you just have to choose the length of the calibration period. Here we take `m` as the first 25 % of the total length of `x1` (i.e. the first 50 observations).

```{R}
test1 <- monitorStationarity(x1, m = 0.25)
```

Now you can print the results:
```{R}
print(test1)
```

...and also plot it. Tell the `plot` method `what` you want to see -- the `"test"` statistics (default) or the `"values"` / `"residuals"` of the tested time series or `"both"` (which will generate two plots at once).
```{R}
plot(test1, what = "test")
```

Let's also have a look at an example, where (obviously) a structural break occurs and the stationarity ends after 100 observations. You may want exactly the first 93 observations to be part of the calibration period -- which is no problem, since you can just specify it via the `m` argument in both ways (fraction of length *and* number of observations).

```{R}
x2 <- c(x1[1:100], cumsum(x1[101:200]) / 2)
test2 <- monitorStationarity(x2, m = 93)
print(test2)
```

As already mentioned above, you can set the `what` argument to `"both"` to get to plots from the model:

```{R, fig.height = 6}
oldpar <- par(mfrow = c(2, 1))
plot(test2, what = "both", legend = FALSE)
par(oldpar)
```



### Monitoring Cointegration

We generate a cointegration model with three regressors and get the response variable as the sum of them plus an intercept term of 1 and a random error. 

```{R}
set.seed(1909)
x1 <- cumsum(rnorm(100, mean = 0.05, sd = 0.1))
x2 <- cumsum(rnorm(100, sd = 0.1)) + 1
x3 <- cumsum(rnorm(100, sd = 0.2)) + 2
x <- cbind(x1, x2, x3)
y <- 1 + x1 + x2 + x3 + rnorm(100, sd = 0.2)
matplot(1:100, cbind(y, x), type = "l", main = "Cointegration Model",
        xlab = "Observation Number", ylab = "")
```

Now, you want to monitor the cointegrating relationship and set the calibration period to 40 observations. There are (at least) two additional questions that you have to think about:

1. Which method to use for estimating the model parameters?  
This is done with the `cointReg` package, so you first of all may have a look at its documentation. There are three possible choices: `"FM"`, `"D"` and `"IM"`.

2. Include a linear trend or only use an intercept?  
For this question it may be helpful to see the results of a "full" `cointReg` model:
```{R}
m <- 40
cointRegFM(x[1:m, ], y[1:m], deter = cbind(level = 1, trend = 1:m))
```

In the calibration period, the linear trend component is not significantly different from 0, so it should be sufficient to set `trend = FALSE`.

```{R}
test3 <- monitorCointegration(x = x, y = y, m = m, model = "FM", trend = FALSE)
print(test3)
```

Again, this can be plotted to get a visual understanding of the results:

```{R, fig.height = 6}
oldpar <- par(mfrow = c(2, 1))
plot(test3, what = "both", legend = FALSE)
par(oldpar)
```

Obviously, the test statistics are always on a very low level and the residuals in the post-calibration period, determined by using the calibration period FM-OLS model, do not differ from its former behaviour.

Finally, we can investigate a "broken" cointegration model, like the following one. It's with the same `x` and `y` values as above, but with an additional random-walk term beginning with observation no. 61.

```{R}
y2 <- y + c(rep(0, 60), cumsum(rnorm(40, sd = 0.5)))
matplot(1:100, cbind(y2, x), type = "l", main = "Cointegration Model",
        xlab = "Observation Number", ylab = "")
abline(v = 60.5, col = 2)
```

Again, we set the argument `trend = FALSE` and have a look at the monitoring results:

```{R}
test4 <- monitorCointegration(x = x, y = y2, m = m, model = "FM", trend = FALSE)
print(test4)
```

We can see, that the procedure is able to detect this structural break on a significant level (p < 0.01).

The plot shows that the test statistic is increasing and crosses the critical value (grey dashed line) at observation no. ```r test4$time```.

```{R, fig.height = 6}
oldpar <- par(mfrow = c(2, 1))
plot(test4, what = "both", legend = FALSE)
par(oldpar)
```

For a detailed view on the test statistics, you can use the base plot argument `log`. It reveals that the test statistic is starting to grow some observations after no. 61, which is the first one after the structural break.

```{R, fig.height = 3}
plot(test4, what = "test", legend = FALSE, log = "y")
```
