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README.Rmd
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---
output: github_document
bibliography: bib.bib
suppress-bibliography: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Deep Vector Autoregression for Macroeconomic Data
This repository contains all the code for @altmeyer2021deep. This research project started off as a master's thesis project, but has since been carried forward and accepted for a poster presentation at the [NeurIPS 2021 MLECON Workshop](https://nips.cc/Conferences/2021/ScheduleMultitrack?event=21847). It is worth flagging that we still consider this very much a work-in-progress - both the research and the companion package. We therefore very much welcome any feedback, suggestions and comments.
For comments regarding the research and methodology, please [open an issue](https://github.com/pat-alt/deepvarsMacro/issues) in this repository. For any concerns regarding the companion package please open an issue [here](https://github.com/pat-alt/deepvars/issues).
### Paper Abstract
*Vector Autoregression is a popular choice for forecasting time series data. Due to its simplicity and success at modelling monetary economic indicators VAR has become a standard tool for central bankers to construct economic forecasts. A crucial assumption underlying the conventional VAR is that interactions between variables through time can be modelled linearly. We propose Deep VAR: a novel approach towards VAR that leverages the power of deep learning in order to model non-linear relatonships. By modelling each equation of the VAR system as a deep neural network, our proposed extension outperforms its conventional benchmark in terms of in-sample fit, out-of-sample fit and point forecasting accuracy. In particular, we find that the Deep VAR is able to better capture the structural economic changes during periods of uncertainty and recession. By staying methodologically as close as possible to the original benchmark, we hope that our approach is more likely to find acceptance in the economics domain.*
## Pointers
A few useful pointers:
- At a glance: NeurIPS 2021 [poster](poster/neurips.pdf) and [video presentation](https://www.youtube.com/watch?v=YRfwsZWf8mI&t=45s).
- More detailed [poster](poster/poster.pdf).
- Even more detailed [slides](presentation/presentation.pdf).
- Full detail: [paper](paper/paper.pdf).
- Code: [R Package](https://github.com/pat-alt/deepvars).
## Citation
Please cite our paper as follows:
```
@article{altmeyer2021deep,
author = {Altmeyer, Patrick and Agusti, Marc and Vidal-Quadras Costa, Ignacio},
date-added = {2021-09-23 13:33:59 +0200},
date-modified = {2021-11-30 16:33:49 +0100},
title = {Deep Vector Autoregression for Macroeconomic Data},
url = {https://thevoice.bse.eu/wp-content/uploads/2021/07/ds21-project-agusti-et-al.pdf},
year = {2021}
}
```
Please cite the companion package as follows:
```
@software{Altmeyer_deepvars_Deep_Vector_2021,
author = {Altmeyer, Patrick},
month = {12},
title = {{deepvars: Deep Vector Autoregression}},
url = {https://github.com/pat-alt/deepvars},
version = {0.1.0},
year = {2021}
}
```