The primary objective of this paper is to revisit DSGE models with a view to bringing out their key weaknesses, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting performance, and potentially misleading policy analysis. It is argued that most of these weaknesses stem from failing to distinguish between statistical and substantive adequacy and secure the former before assessing the latter. The paper untangles the statistical from the substantive premises of inference to delineate the above-mentioned issues and propose solutions. The discussion revolves around a typical DSGE model using US quarterly data. It is shown that this model is statistically misspecified, and when respecified to arrive at a statistically adequate model gives rise to the Student’s t VAR model. This statistical model is shown to (i) provide a sound basis for testing the DSGE overidentifying restrictions as well as probing the identifiability of the deep parameters, (ii) suggest ways to meliorate its substantive inadequacy, and (iii) give rise to reliable forecasts and policy simulations.

中文翻译：

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模型验证和 DSGE 建模

本文的主要目的是重新审视 DSGE 模型，以找出它们的主要弱点，包括统计错误指定、无法识别深层参数、实质性不足、预测性能薄弱以及潜在的误导性政策分析。有人认为，这些弱点大多源于未能区分统计充分性和实质性充分性，并在评估后者之前确保前者。本文从推论的实质前提中解开统计数据，以描述上述问题并提出解决方案。讨论围绕使用美国季度数据的典型 DSGE 模型展开。结果表明，该模型在统计上是错误指定的，并且当重新指定以达到统计上适当的模型时，会产生学生的tVAR模型。该统计模型显示为 (i) 为测试 DSGE 过度识别限制以及探索深层参数的可识别性提供了良好的基础，(ii) 提出了改善其实质性不足的方法，以及 (iii) 产生可靠的预测和政策模拟。