Macroeconomic Data Transformations Matter

08/04/2020
by   Philippe Goulet Coulombe, et al.
0

From a purely predictive standpoint, rotating the predictors' matrix in a low-dimensional linear regression setup does not alter predictions. However, when the forecasting technology either uses shrinkage or is non-linear, it does. This is precisely the fabric of the machine learning (ML) macroeconomic forecasting environment. Pre-processing of the data translates to an alteration of the regularization – explicit or implicit – embedded in ML algorithms. We review old transformations and propose new ones, then empirically evaluate their merits in a substantial pseudo-out-sample exercise. It is found that traditional factors should almost always be included in the feature matrix and moving average rotations of the data can provide important gains for various forecasting targets.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 21

page 24

03/01/2021

Can Machine Learning Catch the COVID-19 Recession?

Based on evidence gathered from a newly built large macroeconomic data s...
01/28/2019

ML for Flood Forecasting at Scale

Effective riverine flood forecasting at scale is hindered by a multitude...
03/11/2020

Machine Learning Treasury Yields

We give explicit algorithms and source code for extracting factors under...
06/14/2020

Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances

Sub-seasonal climate forecasting (SSF) focuses on predicting key climate...
08/28/2020

How is Machine Learning Useful for Macroeconomic Forecasting?

We move beyond "Is Machine Learning Useful for Macroeconomic Forecasting...
09/09/2019

Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms

Daily streamflow forecasting through data-driven approaches is tradition...
04/25/2019

Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)

This paper considers improved forecasting in possibly nonlinear dynamic ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.