Derksen, S. and H. J. Keselman (1992). “Backward, Forward and Stepwise Automated Subset Selection Algorithms: Frequency of Obtaining Authentic and Noise Variables,” British Journal of Mathematical and Statistical Psychology, 45, 265–282.

Escribano, Alvaro and Genaro Sucarrat (2011). “Automated model selection in finance: General-to-specific modelling of the mean and volatility specifications,” Oxford Bulletin of Economics and Statistics, 75, 716–735.

Friedman, J., T. Hastie, and R. Tibshirani. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22.

Hastie, T., R. Tibshirani, and J. Friedman (2009). The Elements of Statistical Learning, Second Edition. New York: Springer.

Hastie, T., R. Tibshirani, and M. Wainwright. (2015). Statistical learning with sparsity: the lasso and generalizations. CRC Press.

Hoover, K. D. and S. J. Perez (1999). “Data Mining Reconsidered: Encompassing and the General-to-Specific Approach to Specification Search,” Econometrics Journal, 2, 167–191.

Hurvich, C. M. and C. L. Tsai (1990). “The Impact of Model Selection on Inference in Linear Regression,” American Statistician, 44, 214–217.

Roecker, E. B. (1991). “Prediction Error and its Estimation for Subset-Selection Models,” Technometrics, 33, 459–469.

Uniejewski B., Nowotarski, J., and Weron, R (2016) “Automated variable selection and shrinkage for day-ahead electricity price forecasting,” mimeo.

Zou, H., and T. Hastie. (2005). “Regularization and variable selection via the elastic net,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320.