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https://github.com/junyuan-chen/autoregressivemodels.jl

Essential toolkits for working with autoregressive models
https://github.com/junyuan-chen/autoregressivemodels.jl

bootstrapping-statistics econometrics economics impulse-response julia statistics time-series

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Essential toolkits for working with autoregressive models

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# AutoregressiveModels.jl



*Essential toolkits for working with autoregressive models*



[![CI-stable][CI-stable-img]][CI-stable-url]

[![codecov][codecov-img]][codecov-url]

[![PkgEval][pkgeval-img]][pkgeval-url]



[CI-stable-img]: https://github.com/junyuan-chen/AutoregressiveModels.jl/actions/workflows/CI-stable.yml/badge.svg?branch=main

[CI-stable-url]: https://github.com/junyuan-chen/AutoregressiveModels.jl/actions/workflows/CI-stable.yml



[codecov-img]: https://codecov.io/gh/junyuan-chen/AutoregressiveModels.jl/branch/main/graph/badge.svg

[codecov-url]: https://codecov.io/gh/junyuan-chen/AutoregressiveModels.jl



[pkgeval-img]: https://juliaci.github.io/NanosoldierReports/pkgeval_badges/A/AutoregressiveModels.svg

[pkgeval-url]: https://juliaci.github.io/NanosoldierReports/pkgeval_badges/A/AutoregressiveModels.html



[AutoregressiveModels.jl](https://github.com/junyuan-chen/AutoregressiveModels.jl)

is a Julia package that provides essential toolkits for working with autoregressive models.

Performance and reusability is prioritized over comprehensive coverage of functionalities,

as a main goal of the package is to provide support

for other packages with more specialized purposes.

At this moment, the main focus is on vector autoregressions (VAR).

Estimation of factor models is implemented for balanced panel data

following Stock and Watson (2016).

Some basic support for the autoregressive-moving-average (ARMA) models is also included.



## Example Usage



To illustrate what the package offers,

here is an example of estimating the impulse responses

based on structural vector autoregressions (SVAR)

and producing a simultaneous confidence band with bootstrap.

Details for individual functions may be found

from docstrings in the help mode of Julia REPL.



### Impulse Responses from Structural VAR



The example below reproduces one application from Montiel Olea and Plagborg-Møller (2019).

The data used are from Gertler and Karadi (2015).



#### Step 1: Model Specification and Point Estimates



```julia

using AutoregressiveModels, CSV, ConfidenceBands

using LocalProjections: datafile # Only needed for the data file



# Load a prepared data file from Gertler and Karadi (2015)

data = CSV.File(datafile(:gk))

# Specify the variables for VAR (the order matters)

names = (:logcpi, :logip, :gs1, :ebp)

# Estimate VAR(12) with OLS and conduct Cholesky factorization for identification

r = fit(VARProcess, data, names, 12, choleskyresid=true, adjust_dofr=false)

# Compute point estimates of impulse responses (37 horizons) to the structural shock (3)

irf = impulse(r, 3, 37, choleskyshock=true)

```



#### Step 2: Bootstrap Confidence Band



A flexible autoregressive bootstrap framework is defined via `bootstrap!`

and can be used to produce the draws of estimates for

`SuptQuantileBootBand()` implemented in

[ConfidenceBands.jl](https://github.com/junyuan-chen/ConfidenceBands.jl):



```julia

# Define how the bootstrap statistics are computed

# See the docstring of bootstrap! for explanations

fillirf!(x) = impulse!(x.out, x.r, 3, choleskyshock=true)

ndraw = 10000

# Preallocate an output array for statistics computed over the bootstrap iterations

bootirfs = Array{Float64, 3}(undef, 4, 37, ndraw)



# Specify the bootstrap procedure

bootstrap!(bootirfs=>fillirf!, r, initialindex=1, drawresid=iidresiddraw!)

# Produce a confidence band from the result

boot2 = view(bootirfs, 2, :, :)

lb, ub, pwlevel = confint(SuptQuantileBootBand(), boot2, level=0.68)

```



#### Step 3: Visualization



Here is a plot for the results with the complete script located

[here](https://raw.githubusercontent.com/junyuan-chen/AutoregressiveModels.jl/main/docs/src/plots/readmeexample.jl):





  





## References



**Gertler, Mark, and Peter Karadi.** 2015.

"Replication Data for: Monetary Policy Surprises, Credit Costs, and Economic Activity."

*American Economic Association* [publisher], Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/E114082V1.



**Montiel Olea, José Luis and Mikkel Plagborg-Møller.** 2019.

"Simultaneous Confidence Bands: Theory, Implementation, and an Application to SVARs."

*Journal of Applied Econometrics* 34 (1): 1-17.



**Stock, James H. and Mark W. Watson.** 2016.

"Chapter 8---Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics."

In *Handbook of Macroeconomics*, Vol. 2A,

edited by John B. Taylor and Harald Uhlig, 415-525. Amsterdam: Elsevier.