Model confidence sets and forecast combination: an application to age-specific mortality.

ABSTRACT:

Background

Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model.

Objective

The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of models before equally averaging forecasts from the selected superior models. Motivated by Hansen et al. (Econometrica 79(2):453-497, 2011), we apply and evaluate the model confidence set procedure when combining mortality forecasts.

Data and methods

The proposed model averaging proce

SUBMITTER: Shang HL 

PROVIDER: S-EPMC6276067 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature