Recent economic crises have posed important challenges for forecasting. Models estimated pre-crisis may perform badly when normal economic relationships have been disrupted. Meanwhile, forecasting, especially in central banks, is increasingly based on a suite of models, following two main approaches: structural (DSGE) and reduced form. The challenge remains to identify which model – or combination of models – is likely to make better forecasts in a changing environment. We explore this issue by assessing the forecasting performance of combinations of a medium-scale DSGE model with standard reduced-form methods applied to the Spanish economy and a reference period that includes both the great recession and the sovereign debt crisis. Our findings suggest that: (i) the mean reverting properties of the DSGE model cause it to underestimate the growth of real variables following the inclusion of crisis episodes in the estimation period; (ii) despite this, reduced-form VARs benefit from the imposition of an economic prior from the structural model; but (iii) pooling information in the form of variables extracted from the structural model with (B)VAR methods does not improve forecast accuracy. By analysing the quantiles of the predictive distributions, we also provide evidence that merging models can help improve the forecast in a context including crisis episodes.

中文翻译：

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合并结构模型和简化模型进行预测

最近的经济危机给预测带来了重大挑战。当正常的经济关系被破坏时，危机前估计的模型可能表现不佳。与此同时，预测，尤其是央行的预测，越来越多地基于一套模型，遵循两种主要方法：结构性（DSGE）和简化形式。挑战仍然是确定哪种模型（或模型组合）可能在不断变化的环境中做出更好的预测。我们通过评估中等规模 DSGE 模型与适用于西班牙经济的标准简化形式方法以及包括大衰退和主权债务危机的参考期的组合的预测性能来探讨这个问题。我们的研究结果表明：（i）DSGE 模型的均值回归特性导致其低估了在估计期间包含危机事件后实际变量的增长； (ii) 尽管如此，简化形式的 VAR 受益于结构模型中经济先验的强加；但是 (iii) 以使用 (B)VAR 方法从结构模型中提取的变量形式汇集信息并不能提高预测精度。通过分析预测分布的分位数，我们还提供了证据，证明合并模型可以帮助改进包括危机事件在内的背景下的预测。