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Generalized forecast averaging in autoregressions with a near unit root

机译：具有近单位根的自动推广预测的平均

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摘要

This paper develops a new approach to forecasting a highly persistent time series that employs feasible generalized least squares (FGLS) estimation of the deterministic components in conjunction with Mallows model averaging. Within a local-to-unity asymptotic framework, we derive analytical expressions for the asymptotic mean squared error and one-step-ahead mean squared forecast risk of the proposed estimator and show that the optimal FGLS weights are different from their ordinary least squares (OLS) counterparts. We also provide theoretical justification for a generalized Mallows averaging estimator that incorporates lag order uncertainty in the construction of the forecast. Monte Carlo simulations demonstrate that the proposed procedure yields a considerably lower finite-sample forecast risk relative to OLS averaging. An application to U.S. macroeconomic time series illustrates the efficacy of the advocated method in practice and finds that both persistence and lag order uncertainty have important implications for the accuracy of forecasts.
机译：本文开发了一种预测高度持久性时间序列的新方法，该持续时间序列采用可行的广义最小二乘（FGLS）估计确定性组件与Mallows模型平均值。在局部到unity渐近框架内，我们导出了渐近均值误差的分析表达式，以及所提出的估计器的一步平均平均预测风险，并表明最佳FGLS权重与其普通最小二乘（OLS ）同行。我们还为广义的Mallows提供了理论性的理由，其平均估算器包含滞后顺序在预测的建设中的不确定性。 Monte Carlo模拟表明，所提出的程序相对于OLS平均产生相当低的有限样本预测风险。美国宏观经济时间序列的应用说明了主张方法在实践中的功效，并发现持久性和滞后顺序不确定性对预测的准确性具有重要意义。

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