Corpus ID: 236965987

Bayesian forecast combination using time-varying features

@inproceedings{Li2021BayesianFC,
  title={Bayesian forecast combination using time-varying features},
  author={Li Li and Yanfei Kang and Feng Li},
  year={2021}
}
  • Li Li, Yanfei Kang, Feng Li
  • Published 2021
  • Economics, Mathematics
In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time-varying features. Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal forecast combination is determined by time series features from historical information. In particular, we use an automatic Bayesian variable selection method to identify the importance of different features. To this end, our approach has… Expand

References

SHOWING 1-10 OF 72 REFERENCES
FFORMA: Feature-based forecast model averaging
TLDR
This work uses a collection of time series to train a meta-model for assigning weights to various possible forecasting methods with the goal of minimizing the average forecasting loss obtained from a weighted forecast combination. Expand
Combining forecast densities from VARs with uncertain instabilities
Recursive-weight forecast combination is often found to an ineffective method of improving point forecast accuracy in the presence of uncertain instabilities. We examine the effectiveness of thisExpand
CONSTRUCTING OPTIMAL DENSITY FORECASTS FROM POINT FORECAST COMBINATIONS
SUMMARY Decision makers often observe point forecasts of the same variable computed, for instance, by commercial banks, IMF and the World Bank, but the econometric models used by such institutionsExpand
Dynamic Bayesian predictive synthesis in time series forecasting
TLDR
A foundational Bayesian perspective based on agent opinion analysis theory defines a new framework for density forecast combination, and encompasses several existing forecast pooling methods, that develops a novel class of dynamic latent factor models for time series forecast synthesis. Expand
Combining inflation density forecasts
In this paper, we empirically evaluate competing approaches for combining inflation density forecasts in terms of Kullback-Leibler divergence. In particular, we apply a similar suite of models toExpand
Forecast Combinations
Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simpleExpand
The Evolution of Forecast Density Combinations in Economics
Increasingly, professional forecasters and academic researchers in economics present model-based and subjective or judgment-based forecasts that are accompanied by some measure of uncertainty. In itsExpand
Real‐time inflation forecast combination for time‐varying coefficient models
  • B. Zhang
  • Economics
  • Journal of Forecasting
  • 2018
We use real‐time macroeconomic variables and combination forecasts with both time‐varying weights and equal weights to forecast inflation in the USA. The combination forecasts compare three sets ofExpand
Combining density forecasts
This paper brings together two important but hitherto largely unrelated areas of the forecasting literature, density forecasting and forecast combination. It proposes a practical data-driven approachExpand
A Note on Estimation of Optimal Weights for Density Forecast Combinations
The problem of finding appropriate weights for combining several density forecasts is an important issue that is currently being debated in the forecast combination literature. A recent paper by HallExpand
...
1
2
3
4
5
...