POOLING VERSUS MODEL SELECTION FOR NOWCASTING GDP WITH MANY PREDICTORS: EMPIRICAL EVIDENCE FOR SIX INDUSTRIALIZED COUNTRIES
多预测变量下 GDP 即时预测的模型合并与选择：六个工业化国家的证据AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究季度 GDP 即时预测，并将大数据因子模型作为主要比较对象之一。
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多预测变量下 GDP 即时预测的模型合并与选择：六个工业化国家的证据
- 研究问题
- 面对发布延迟、混合频率和大量指标导致的模型不确定性，季度 GDP 即时预测采用模型选择还是合并预测更稳定？
- 方法与思路
- 作者比较大数据因子模型与少量预测变量的 MIDAS 回归，在多种模型设定上评估信息准则顺序选择及预测合并。
- 材料陈述的贡献
- 摘要称，单模型表现随时间明显变化，选择方法有时优于基准但结果依赖规则，而合并预测总体呈现较稳定的跨期表现。
- 阅读关注点
- 应核对实时信息集与发布延迟处理、因子和指标选择、合并权重，以及预测评估是否存在数据修订或信息泄漏问题。
- 阅读边界
- 仅依据所给 Crossref 摘要。摘要未列出国家、评估窗口及误差数值，稳定性不等于每个时期、每个国家均具有最优精度。
原文摘要
SUMMARY This paper discusses pooling versus model selection for nowcasting with large datasets in the presence of model uncertainty. In practice, nowcasting a low‐frequency variable with a large number of high‐frequency indicators should account for at least two data irregularities: (i) unbalanced data with missing observations at the end of the sample due to publication delays; and (ii) different sampling frequencies of the data. Two model classes suited in this context are factor models based on large datasets and mixed‐data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst other things, the factor estimation method and the number of factors, lag length and indicator selection. Thus there are many sources of misspecification when selecting a particular model, and an alternative would be pooling over a large set of different model specifications. We evaluate the relative performance of pooling and model selection for nowcasting quarterly GDP for six large industrialized countries. We find that the nowcast performance of single models varies considerably over time, in line with the forecasting literature. Model selection based on sequential application of information criteria can outperform benchmarks. However, the results highly depend on the selection method chosen. In contrast, pooling of nowcast models provides an overall very stable nowcast performance over time. Copyright © 2012 John Wiley & Sons, Ltd.