Next Article in Journal
A q-Extension of Sigmoid Functions and the Application for Enhancement of Ultrasound Images
Next Article in Special Issue
Assessing the Performance of Hierarchical Forecasting Methods on the Retail Sector
Previous Article in Journal
Thermodynamic Investigation of an Integrated Solar Combined Cycle with an ORC System
Previous Article in Special Issue
Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

A Data-Weighted Prior Estimator for Forecast Combination

1
REGIOlab and Department of Applied Economics, University of Oviedo, Faculty of Economics and Business, Avda. del Cristo, s/n, 33006 Oviedo, Spain
2
Regional Economics Applications Laboratory, University of Illinois at Urbana-Champaign 607 S. Matthew, Urbana, IL 61801-367, USA
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(4), 429; https://doi.org/10.3390/e21040429
Received: 7 February 2019 / Revised: 11 April 2019 / Accepted: 18 April 2019 / Published: 23 April 2019
(This article belongs to the Special Issue Entropy Application for Forecasting)
  |  
PDF [283 KB, uploaded 23 April 2019]
  |  

Abstract

Forecast combination methods reduce the information in a vector of forecasts to a single combined forecast by using a set of combination weights. Although there are several methods, a typical strategy is the use of the simple arithmetic mean to obtain the combined forecast. A priori, the use of this mean could be justified when all the forecasters have had the same performance in the past or when they do not have enough information. In this paper, we explore the possibility of using entropy econometrics as a procedure for combining forecasts that allows to discriminate between bad and good forecasters, even in the situation of little information. With this purpose, the data-weighted prior (DWP) estimator proposed by Golan (2001) is used for forecaster selection and simultaneous parameter estimation in linear statistical models. In particular, we examine the ability of the DWP estimator to effectively select relevant forecasts among all forecasts. We test the accuracy of the proposed model with a simulation exercise and compare its ex ante forecasting performance with other methods used to combine forecasts. The obtained results suggest that the proposed method dominates other combining methods, such as equal-weight averages or ordinal least squares methods, among others. View Full-Text
Keywords: data-weighted prior; generalized maximum entropy method; combined forecast data-weighted prior; generalized maximum entropy method; combined forecast
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Fernández-Vázquez, E.; Moreno, B.; Hewings, G.J. A Data-Weighted Prior Estimator for Forecast Combination. Entropy 2019, 21, 429.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top