VectorAutoregressions.jl: Vector Autoregressive (VAR) models in Julia.
ForecastingCombinations.jl: forecasting using a combinatoric approach and exploiting parallel computing in Julia.
Forecasting Variables using a combinatoric approach and exploiting parallel computing in Julia (ForecastingCombinations.jl) Installation Pkg.clone("https://github.com/lucabrugnolini/ForecastingCombinations.jl") Documentation The procedure is described in Brugnolini L. (2018). The application in the paper is on predicting the probability of having inflation around the European Central Bank’s target.
Introduction Given a (balanced) dataset of K macroeconomic variables, the objective is to select the best model to predict future values of a target variable. The selection procedure consists in (i) select the best iBest variables according to several out-of-sample criteria and then use these variables in models that use their combination.
Vector autoregressive models for Julia (VAR.jl) Installation Pkg.clone("https://github.com/lucabrugnolini/VectorAutoregressions.jl") Introduction This package is a work in progress for the estimation and identification of Vector Autoregressive (VAR) models.
Status VAR VAR(1) form Lag-length selection AIC AICC BIC HQC VAR impulse response function (IRFs) Identification Reduce form Cholesky Long-run restrictions Sign restrictions Heteroskedasticity External instruments (ex. high-frequency,narrative) Confidence bands Asymptotic Bootstrap Bootstrap-after-bootstrap Forecasting BVAR FAVAR Local projection IRFs Lag-length selection Confidence bands Standard Bootstrap Bayesian Local Projection Example ## Example: fit a VAR(`p`) to the data and derive structural IRFs with asymptotic and bootstrap conf.