ForecastingInflation

Forecasting Inflation in a data-rich environment: the benefits of machine learning methods

• Required packages:

  • HDeconometrics (from my github)
  • h20
  • glmnet
  • xgboost
  • randomForest
  • lbvar (from my github)
  • boot

• This repository contains the codes used to run the rolling windows in the paper "Forecasting Inflation in a data-rich environment: the benefits of machine learning methods" by Medeiros, Vasconcelos, Veiga and Zilberman.

• Codes are divided in one folder for each subsample used in the paper.

• Each folder contains one folder with functions and one folder with codes to call and run the functions. PATHS MUST BE ADJUSTED.

• If the paths are correct, running the files in the RUN folders will start the rolling windows.

• Data are already treated as descriped in the paper.

References

Medeiros, M. C., Vasconcelos, G., Veiga, A., & Zilberman, E. (2018). Forecasting Inflation in a data-rich environment: the benefits of machine learning methods.