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V-Dem's Forecasting Project
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Data_management added data management stuff Nov 22, 2019
ForecastApp Added the shiny app Dec 10, 2019
Models move model code to Models/ folder Dec 17, 2019
.gitignore unpack regime-forecast folder Nov 15, 2019
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Predicting Adverse Regime Transitions (PART)

README last compiled on: 2019-12-17

Data and code for the V-Dem VForecast/PART project to predict the risk of adverse regime transitions.

Note from Andy (mosty for future Andy and Rick): The original work in late 2018 and early 2019 leading up to the May 2019 Policy Day was all done on Dropbox in the regime-forecast folder. I copied some of the contents of that folder here, and also bumped up one level a smaller partial copy of regime-forecast that was in this repo from before.


The Data_management folder contains all of the R scripts necessary for data organization. Due to the size of the data files, we cannot share them through this repo. However, you can find the finished product, ALL_data_final_USE_v9.csv, in the Models/input folder. For this project we use V-Dem V9 along with a number of external data sources (See: Data_management/compile_external_data.R). Please contact Andy for access to these data.

The Models folder contains the scripts to estimate models. To run the models, see the train-model....R R scripts in the scripts folder. It should be possible to run all of the independently as long as the neccessary packages listed at the top of each file are installed. The working directory should be the Models folder under this repo/project, i.e. basename(getwd()) should be Models.

Each model runner script depends on the input data in the input folder, and on the 0-setup-training-environment.R script to setup data and other joint parameters shared by all the models.

The output folder contains copies of the output from when we ran the full set of models the last time before the May 2019 V-Dem Policy Day. Cross-validation is used to tune and assess models. We did not set seeds when running this, so some variation in output might be expected from randomness in the CV data partitions.

AB 2019-11-25: I made minimal changes to be able to run train-model1.R from the GH repo. I only checked that script as the other models can take a while to run. I noticed that the output changes slightly, not sure why.

The ForecastApp folder contains the code for the Shiny dashboard on the V-Dem website here

Related repos

  • andybega/part has one of the PART papers
  • andybega/vfcast is a tiny R package that had paths to Dropbox for Andy and Rick, to avoid having some path finding logic at the top of every single script. Not really needed for git so I might delete it.
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