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用于预测组合的 Bregman 模型平均AI 解读

We propose a unified model averaging (MA) approach for a broad class of forecasting targets. This approach is established by minimizing an asymptotic risk based on the expected Bregman divergence of a combined forecast, relative to the optimal forecast of the forecasting target, under local(-to-zero) asymptotics. It can be flexibly applied to develop effective MA methods across various forecasting contexts, including but not limited to univariate and multivariate mean…

可能非平稳自回归的模型平均预测AI 解读

As an alternative to model selection (MS), this paper considers model averaging (MA) for integrated autoregressive processes of infinite order (AR(∞)). We derive a uniformly asymptotic expression for the mean squared prediction error (MSPE) of the averaging prediction with fixed weights and then propose a Mallows-type criterion to select the data-driven weights that minimize the MSPE asymptotically. We show that the proposed MA estimator and its variants, Shibata and Akaike…

构造渐近最优组合预测的模型平均方法AI 解读

We propose a model-averaging (MA) method for constructing an asymptotically optimal combination of a set of point forecast sequences generated from a class of predictive regressions. The asymptotic optimality is defined in terms of approximating an unknown conditional-mean sequence based on the local(-to-zero) asymptotics. Our method has the following essential features. First, it is more general than combining a set of single point forecasts. Second, the asymptotic…

预测回归中的模型平均AI 解读

In this paper, we consider forecast combination in a predictive regression. We construct the point forecast by combining predictions from all possible linear regression models, given a set of potentially relevant predictors. We derive the asymptotic risk of least‐squares averaging estimators in a local asymptotic framework. We then develop a frequentist model averaging criterion, an asymptotically unbiased estimator of the asymptotic risk, to select forecast weights. Monte…