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该项目涉及构建一类新的检验统计量，可用于检验广义矩法 (GMM) 框架中的假设。这个新类别中的统计不需要直接估计时刻条件的长期方差（频率为零的谱密度）。新方法很可能会产生比当前可用的测试具有更好的有限样本属性的一类测试。这将是一项重要贡献，并且应该会对宏观经济学和金融领域的实证工作产生积极影响，原因有两个。首先，GMM框架在实证工作中被广泛使用。应用包括经济周期模型的估计、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，应用于 GMM 模型的标准检验的有限样本属性很差。尺寸往往会夸大，而功率可能会很低。因此，GMM 框架中的推理可能非常不精确。新方法应该提供替代测试，以给出更精确的推论。这些测试可能会吸引相对较多的用户，因为新的测试易于计算，并且不需要从业者做出诸如核、截断滞后、自动截断滞后逼近模型和权重、滞后长度等选择。由于推理可能对这些选择敏感，因此新方法为从业者提供了一个简单且更强大的测试框架。