A New Approach to Testing in the Generalized Method of Moments Framework

广义矩量法框架中测试的新方法

• 批准号: 9818695
• 负责人: Timothy Vogelsang
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-06-01 至 2000-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9818695&HistoricalAwards=false
• 关键词: New; Approach; Testing; Generalized; Method

Timothy VogelsangSBR-9818695This project is concerned with constructing a new class of test statistics that can be used to test hypotheses in the generalized method of moments (GMM) framework. The statistics in this new class do not require direct estimates of the long run variance (spectral density at frequency zero) of the moment conditions. It is likely the new approach will result in a class of tests that have better finite sample properties than currently available tests. This will be an important contribution and should positively impact empirical work in macroeconomics and finance for two reasons. First, the GMM framework is widely used in empirical work. Applications include estimation of business cycle models, stochastic volatility models, asset-pricing models, estimation of covariance structures, etc. Second, there is considerable evidence that standard tests applied to GMM models have poor finite sample properties. Size is often inflated, and power can be low. Thus, inference in the GMM framework can be very imprecise. The new approach should provide alternative tests that give more precise inference. The tests are likely to attract a relatively large club of users because the new tests are easy to compute and do not require the practitioner to make such choices as kernel, truncation lag, automatic truncation lag approximating models and weights, lag lengths, etc. Because inference can be sensitive to those choices, the new approach provides practitioners with a simple more robust testing framework.

Timothy Vogelsangsbr-9818695这个项目涉及构建新的测试统计数据，该统计量可用于以广义的矩（GMM）框架来测试假设。该新类中的统计数据不需要直接估计矩情况的长期差异（频率为零的光谱密度）。新方法可能会导致一类测试比当前可用的测试具有更好的有限样本属性。这将是一个重要的贡献，应该对宏观经济和金融方面的经验工作积极影响，原因有两个。首先，GMM框架广泛用于经验工作。应用包括对商业周期模型的估计，随机波动率模型，资产定价模型，协方差结构的估计等。其次，有大量证据表明，适用于GMM模型的标准测试的有限样本属性差。尺寸通常会膨胀，功率可能很低。因此，GMM框架中的推断可能非常不精确。新方法应提供更精确推理的替代测试。这些测试可能会吸引相对较大的用户俱乐部，因为新测试易于计算，并且不需要从业者做出诸如内核，截断滞后，自动截断滞后滞后滞后差近似模型和重量，滞后长度等等选择，因为推理可以对这些选择敏感，所以新方法可以为实践提供了更简单的测试框架。