https://github.com/kvasilopoulos/exuber
Econometric Analysis of Explosive Time Series
https://github.com/kvasilopoulos/exuber
dickey-fuller explosive-dynamics simulation time-series
Last synced: 3 months ago
JSON representation
Econometric Analysis of Explosive Time Series
- Host: GitHub
- URL: https://github.com/kvasilopoulos/exuber
- Owner: kvasilopoulos
- License: gpl-3.0
- Created: 2018-01-11T16:43:48.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2025-09-19T07:32:37.000Z (4 months ago)
- Last Synced: 2025-09-19T09:36:43.252Z (4 months ago)
- Topics: dickey-fuller, explosive-dynamics, simulation, time-series
- Language: R
- Homepage: https://kvasilopoulos.github.io/exuber/
- Size: 76.3 MB
- Stars: 29
- Watchers: 3
- Forks: 10
- Open Issues: 3
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Support: .github/SUPPORT.md
Awesome Lists containing this project
README
---
output:
github_document:
html_preview: true
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/"
)
```
# exuber 
[](https://CRAN.R-project.org/package=exuber)
[](https://www.repostatus.org/#active)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/kvasilopoulos/exuber/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/kvasilopoulos/exuber)
Testing for and dating periods of explosive dynamics (exuberance) in time series
using the univariate and panel recursive unit root tests proposed by [Phillips et al. (2015)](https://doi.org/10.1111/iere.12132) and [Pavlidis et al. (2016)]( https://doi.org/10.1007/s11146-015-9531-2). The recursive least-squares algorithm utilizes the matrix inversion lemma to avoid matrix inversion which results in significant speed improvements. Simulation of a variety of periodically-collapsing bubble processes.
### Overview
Testing for explosive dynamics is comprised of two distinct parts :
* Estimation
* Critical Values
**Some Context:** Conventional testing techniques compute critical values,and p-values from a standard distribution, where the user does not need to specify critical values explicitly. However, the recent literature in explosive dynamics require the use of non-standard distributions, which require the use of techniques that sample empirical distributions in order to calculate the critical values.
#### Estimation
The cornerstone function of the package is:
* `radf()`: Recursive Augmented Dickey-Fuller Test.
This function offers a vectorized estimation (i.e. single and/or multiple time-series)
for individual and panel estimation. The estimation can parse data from multiple classes and handle dates as index.
#### Critical Values
There are several options for generating critical values:
* `radf_mc_cv()`: Monte Carlo
* `radf_wb_cv()`: Wild Bootstrap
* `radf_sb_cv()`: Sieve Bootstrap (Panel)
On default `exuber` will use Monte Carlo simulated critical values if no other option is provided. The package offers these critical values in the form of `data` (up to 600 observations), that are obtained with the `mc_cv()` function.
### Analysis
For the analysis you should include both the output from estimation (`object`) and critical values (`cv`). The below methods break the process into small simple steps:
* `summary()` summarizes the model.
* `diagnostics()` shows which series reject the null hypothesis .
* `datestamp()` computes the origination, termination and duration of episodes (if any).
These combined provide a comprehensive analysis on the exuberant behavior of the model.
### Installation
```{r installation, eval = FALSE}
# Install release version from CRAN
install.packages("exuber")
```
You can install the development version of exuber from GitHub.
```{r installation-dev, eval = FALSE}
# install.packages("devtools")
devtools::install_github("kvasilopoulos/exuber")
```
If you encounter a clear bug, please file a reproducible example on [GitHub](https://github.com/kvasilopoulos/exuber/issues).
### Usage
```{r usage}
library(exuber)
rsim_data <- radf(sim_data)
summary(rsim_data)
diagnostics(rsim_data)
datestamp(rsim_data)
autoplot(rsim_data)
```
---
Please note that the 'exuber' project is released with a [Contributor Code of Conduct](https://kvasilopoulos.github.io/exuber/CODE_OF_CONDUCT). By contributing to this project, you agree to abide by its terms.