Macroeconomic and Financial Forecasting Toolbox

Code library for financial and macroeconomic forecasting in MATLAB (Python and R versions in progress)

Contact Victor Sellemi (vsellemi@ucsd.edu) for more information. Please use with attribution.

Supported Models

• AR: autoregressive model
• ARDI: factor-augmented autoregressive model
• PLS: partial least squares
• ENET: elastic net
• LASSO: least absolute shrinkage and selection
• KRR: kernel ridge regression
• SVR: support vector regression
• RF: random forest ensemble
• NN: feed-forward neural networks
• TVPSV: time-varying parameter stochastic volatility
• MS: markov switching
• CSR: complete subset regression

References

P .G. Coulombe, M.Leroux, D.Stevanovic, and S.Surprenant. How is machine learning useful for macroeconomic forecasting?, 2020.

G. Elliott, A.Gargano, and A.Timmermann. Complete subset regressions. Journal of Econometrics, 2013.

P. R. Hansen. A test for superior predictive ability. Journal of Business and Economic Statistics, 2005.

P. R. Hansen, A. Lunde, and J.M. Nason. The model confidence set, Econometrica, 2011.

T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning, Springer Series in Statistics, 2001.

M. W. McCracken and S. Ng. Fred-md: A monthly database for macroeconomic research. Journal of Business & Economic Statistics, 2016.

D.Pettenuzzo and A. Timmermann. Forecasting macroeconomic variables under model instability. Journal of Business and Economic Statistics, 2015.

J. Stock and M. Watson. Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics, 2002.

H. White. A reality check for data snooping. Econometrica, 2000.