This paper studies a procedure to combine individual forecasts that achieve theoretical optimal performance. The results apply to a wide variety of loss functions and no conditions are imposed on the data sequences and the individual forecasts apart from a tail condition. The theoretical results show that the bounds are also valid in the case of time varying combination weights, under specific ...

The relationship of forecast combination to recent developments in the theory and practice of model selection (in particular, the encompassing literature) is explored, the pragmatic virtues of forecast combination are argued, and a synthesis is attempted. Promising areas for future research, particularly exploration and development of forecast combination methodologies that facilitate ‘shrinkag...

This paper proposes a Bayesian procedure for combining forecasts from multivariate forecasting models, e.g. VAR models. Standard applications of Bayesian model averaging suffer from a basic difficulty in this context, when additional variables are included and modelled the connection between the overall measure of fit for the model and the expected forecasting performance for the variables of i...

In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on features. Our estimates in the via Bayesian log predictive scores, which optimal is determined time series features from historical information. particular, use an automatic variable selection method to identify importance of different To end, our approach has better interpr...

inflation forecasting has been one of the requirements for the implementation of monetary policy in countries which their monetary authorities are pursuing inflation targeting regime. however, owing to central bank independence in one hand and as well as the lagged effects of monetary policies on inflation in the other, the monetary authorities should have the sound perspective about the future...

This paper studies forecasts when the forecast loss function is asymmetric, using Australian unemployment rates as an example. We focus on simple univariate models including autoregressive models and self-exciting threshold autoregressive models and we employ the same asymmetric quadratic loss function that is used for forecast evaluation at the model selection, model estimation and forecast co...

Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is c...

Density forecast combinations are becoming increasingly popular as a means of improving forecast ‘accuracy’, as measured by a scoring rule. In this paper we generalise this literature by letting the combination weights follow more general schemes. Sieve estimation is used to optimise the score of the generalised density combination where the combination weights depend on the variable one is try...

In applied forecasting, there is a trade-off between in-sample fit and out-ofsample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, there is often predictable information in forecast errors that is difficult to exploit. However, we show how this predictable information can be exploited in forecast combinations. In this case, o...