# Intro to exuber" In exuber: Econometric Analysis of Explosive Time Series

```knitr::opts_chunk\$set(
collapse = TRUE,
comment = "#>",
message=FALSE
)
options(cli.width = 80)
```
```library(exuber)
```

For our analysis we are going to use the `datasets::EuStockMarkets` dataset, which contains the daily closing prices of four major European stock indices: Germany DAX, Switzerland SMI, France CAC, and UK FTSE (see `?EuStockMarkets`). The data are sampled in business time, i.e., weekends and holidays are omitted. In this particular exercise we want to focus on weekly observations. To do so we aggregate to a weekly frequency and reduce the number of observations from 1860 to 372.

```stocks <- aggregate(EuStockMarkets, nfrequency = 52, mean)
```

## Estimation

We estimate the above series using the recursive Augmented Dickey-Fuller test with 1 lag.

```est_stocks <- radf(stocks, lag = 1)
```

## Analysis

The summary will print the test statistic and the critical values for 10%, 5% and 1% significance level. The package provides simulated critical values for up to 600 observations, so we use them by omitting the `cv` argument in the `summary` function.

```summary(est_stocks)
```

It seems that all stocks exhibit exuberant behaviour but we can also verify it using `diagnostics()`. This function is particularly useful when we deal a large number of series.

```diagnostics(est_stocks)
```

If we need to know the exact period of exuberance we can do so with the function `datestamp()`. `datestamp()` works in a similar manner with `summary()` and `diagnostics()`. The user still has to specify the critical values, however we can still utilize the package's critical values by leaving the cv-argument blank.

```# Minimum duration of an explosive period
rot = psy_ds(stocks) # log(n) ~ rule of thumb

dstamp_stocks <- datestamp(est_stocks, min_duration = rot)
dstamp_stocks
```

We can extract the datestamp as a dummy variable 1 = Exuberance, 0 = No exuberance.

```dummy <- attr(dstamp_stocks, "dummy")
tail(dummy)
```

## Plotting

The `autoplot` function returns a faceted ggplot2 object for all the series that reject the null hypothesis at 5% significance level.

```autoplot(est_stocks)
```

Finally, we can plot just the periods the periods of exuberance. Plotting datestamp object is particularly useful when we have a lot of series, and we are interested to identify explosive patterns in all of them.

```datestamp(est_stocks) %>%
autoplot()
```

## Try the exuber package in your browser

Any scripts or data that you put into this service are public.

exuber documentation built on Dec. 18, 2020, 9:06 a.m.