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README.Rmd
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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, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# Heterogeneous AutoRegressive (HAR) model
<!-- badges: start -->
<!-- [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental) -->
[![CRAN status](https://www.r-pkg.org/badges/version/haR)](https://CRAN.R-project.org/package=haR)
[![devel version](https://img.shields.io/badge/devel%20version-0.1.0-blue.svg)](https://github.com/daniGiro/haR)
<!-- badges: end -->
Tools for estimating and forecasting using the Heterogeneous AutoRegressive (HAR) model by Corsi (2009) [<doi:10.1093/jjfinec/nbp001>](https://doi.org/10.1093/jjfinec/nbp001) and the Partial-Variances Heterogeneous AutoRegressive model by Bollerslev et al. (2022) [<doi:10.1016/j.jeconom.2021.04.013>](https://doi.org/10.1016/j.jeconom.2021.04.013).
## Installation
You can install the development version of **haR** from [GitHub](https://github.com/danigiro/haR) with:
``` r
# install.packages("devtools")
devtools::install_github("daniGiro/haR")
```
## Example
```{r example}
library(haR)
x <- rnorm(1000)
obj <- har(x)
summary(obj)
#coef(obj)
#print(obj)
#fitted(obj)
#residuals(obj)
#predict(obj)
```