Forecasting Unemployment via Machine Learning: The use of Average Windows Forecasts

37 Pages Posted: 17 Dec 2019 Last revised: 4 Apr 2022

See all articles by Tsung-Wu Ho

Tsung-Wu Ho

National Taiwan Normal University, College of Management

Date Written: April 3, 2022

Abstract

The aim of this study is to find a better approach to forecast unemployment rate, we apply the average windows forecasts (AveW) of Pesaran and Pick (2011) to both machine learning and standard ARMA-based time series approaches; moreover, to account for model uncertainties, we average all model-specific AveWs and show that the resulting forecasting combination further improves forecasting. Specifically, our performance comparison shows that: Firstly, for case without covariates, machine learning methods significantly outperform standard time series models, which is also confirmed by the Diebold-Mariano test. Secondly, for case with covariates, both ARMA-based and machine learning methods perform equally well. Finally, surprisingly, the well-known deep learning method, RNN-LSTM, does not have good performance as expected. Overall, AveW is a useful framework for time series forecasting via machine learning.

Keywords: Forecasting time series, forecasting averaging, machine learning, training by rolling k-fold cross validation

JEL Classification: C53

Suggested Citation

Ho, Tsung-Wu, Forecasting Unemployment via Machine Learning: The use of Average Windows Forecasts (April 3, 2022). Available at SSRN: https://ssrn.com/abstract=3496138 or http://dx.doi.org/10.2139/ssrn.3496138

Tsung-Wu Ho (Contact Author)

National Taiwan Normal University, College of Management ( email )

No. 31, Shi-Da Road
Taipei, 10610
Taiwan
+886(2)7734-5624 (Phone)

HOME PAGE: http://web.ntnu.edu.tw/~tsungwu/

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