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Julia implementation of multi-variate time series models, such as vector autoregressive (VAR) and vector error correction (VECM) models.

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Multi-variate Time Series Model in julia

Julia implementation for a family of 'classical' estimation approach of multi-variate time series model. Current applications includes vector autoregressive (VAR(p)) model and its relative estimation procedures. See below for a current selection. Sub-folders in this repository includes source function codes and example application using publicly available data.

For the Bayesian implementation of the equivalent models, estimations, and forecasting frameworks, please visit my other repository for Bayesian Vector Autoregressive model suite.

Section unchecked are currently in development. This repository will be polished and improved as I develop and translate.

Model Estimation

Projections

Decompositions

See functions within each models for applications. Based on the model identifications, computing the decomposition methods may vary.

  • Variance decomposition
  • Historical decomposition

Example Applications

To test the functions and show application, I use following published or common macroeconometric models and frameworks.