The Use of Machine Learning in Treatment Effect Estimation
4.6 Concluding Remarks
In this chapter we review the most recent advances in using Machine Learning models/methods to forecast time-series data in a high-dimensional setup, where the number of variables used as potential predictors is much larger than the available sample to estimate the forecasting models.
We start the chapter by discussing how to construct and compare forecasts from different models. More specifically, we discuss the Diebold-Mariano test of equal predictive ability and the Li-Liao-Quaedvlieg test of conditional superior predictive ability. Finally, we illustrate how to construct model confidence sets.
References 147 In terms of linear ML models, we complement the techniques described in Chapter 1 by focusing of factor-based regression, the combination of factors and penalized regressions and ensemble methods.
After presenting the linear models, we review neural network methods. We discuss both shallow and deep networks, as well as long shot term memory and convolution neural networks.
We end the chapter by discussing some hybrid methods and new proposals in the forecasting literature.
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