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A new approach to multi-step forecasting using dynamic stochastic general equilibrium models

机译:动态随机一般均衡模型的多步预测新方法

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DSGE models are of interest because they offer structural interpretations, but are also increasingly used for forecasting. Estimation often proceeds by methods which involve building the likelihood by one-step ahead (h = 1) prediction errors. However in principle this can be done using different horizons where h > 1. Using the well-known model of Smets and Wouters (2007), for h = 1 classical ML parameter estimates are similar to those originally reported. As h extends some estimated parameters change, but not to an economically significant degree. Forecast performance is often improved, in several cases significantly. (C) 2015 The Bank of England. Published by Elsevier B.V. All rights reserved.
机译:DSGE模型令人感兴趣,因为它们提供结构解释,但也越来越多地用于预测。估计通常采用以下方法进行:涉及通过提前一步(h = 1)预测误差来建立似然性。但是原则上,这可以使用h> 1的不同范围来完成。使用Smets and Wouters(2007)的著名模型,对于h = 1,经典ML参数估计与最初报道的类似。随着h的扩展,一些估计的参数会发生变化,但不会在经济上显着变化。预测性能通常会提高,在某些情况下会显着提高。 (C)2015英格兰银行。由Elsevier B.V.发布。保留所有权利。

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