From 143d3adcf06ae79a9f051bf59932251b15173153 Mon Sep 17 00:00:00 2001
From: n-kall <33577035+n-kall@users.noreply.github.com>
Date: Tue, 17 Sep 2024 15:08:21 +0300
Subject: [PATCH] fix ARR2 path and reference

---
 autoregression/arr2.qmd |  4 ++--
 references.bib          | 16 ++++++++++++++++
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/autoregression/arr2.qmd b/autoregression/arr2.qmd
index eda17fa..95304cf 100644
--- a/autoregression/arr2.qmd
+++ b/autoregression/arr2.qmd
@@ -4,7 +4,7 @@ title: ARR2
 
 ## Description
 
-Like the R2-D2 prior [@r2d2] but for autoregression.
+Developed by [@kohnsARR2PriorFlexible2024a], it is similar to the R2-D2 prior [@zhangBayesianRegressionUsing2022a] but for autoregression.
 
 ## Definition
 
@@ -26,5 +26,5 @@ $$
 
 ## Stan code
 
-```{.stan include="stan/arr2.stan"}
+```{.stan include="../stan/arr2.stan"}
 ```
diff --git a/references.bib b/references.bib
index 6100f21..15cdd35 100644
--- a/references.bib
+++ b/references.bib
@@ -1,3 +1,19 @@
+@online{kohnsARR2PriorFlexible2024a,
+  title = {The {{ARR2}} Prior: Flexible Predictive Prior Definition for {{Bayesian}} Auto-Regressions},
+  shorttitle = {The {{ARR2}} Prior},
+  author = {Kohns, David and Kallioinen, Noa and McLatchie, Yann and Vehtari, Aki},
+  date = {2024-05-31},
+  eprint = {2405.19920},
+  eprinttype = {arXiv},
+  eprintclass = {econ, stat},
+  url = {http://arxiv.org/abs/2405.19920},
+  urldate = {2024-09-17},
+  abstract = {We present the ARR2 prior, a joint prior over the auto-regressive components in Bayesian time-series models and their induced \$R\textasciicircum 2\$. Compared to other priors designed for times-series models, the ARR2 prior allows for flexible and intuitive shrinkage. We derive the prior for pure auto-regressive models, and extend it to auto-regressive models with exogenous inputs, and state-space models. Through both simulations and real-world modelling exercises, we demonstrate the efficacy of the ARR2 prior in improving sparse and reliable inference, while showing greater inference quality and predictive performance than other shrinkage priors. An open-source implementation of the prior is provided.},
+  pubstate = {prepublished},
+  keywords = {Economics - Econometrics,Statistics - Computation}
+}
+
+
 @article{vanzwetDefaultPriorRegression2018,
   title = {A Default Prior for Regression Coefficients},
   author = {{van Zwet}, Erik},