FIXED-SMOOTHING ASYMPTOTICS IN A TWO-STEP GENERALIZED METHOD OF MOMENTS FRAMEWORK

• 成果类型: Article
• 署名作者: Sun, Yixiao
• 署名单位: University of California System; University of California San Diego
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA11684
• 发表日期: 2014
• 页码: 2327-2370
• 关键词: consistent covariance-matrix; AUTOCORRELATION ROBUST-TESTS; RUN VARIANCE-ESTIMATION; ORIGIN KERNELS; time-series; heteroskedasticity; inference; Truncation; regression; estimator

摘要

This paper develops the fixed-smoothing asymptotics in a two-step generalized method of moments (GMM) framework. Under this type of asymptotics, the weighting matrix in the second-step GMM criterion function converges weakly to a random matrix and the two-step GMM estimator is asymptotically mixed normal. Nevertheless, the Wald statistic, the GMM criterion function statistic, and the Lagrange multiplier statistic remain asymptotically pivotal. It is shown that critical values from the fixed-smoothing asymptotic distribution are high order correct under the conventional increasing-smoothing asymptotics. When an orthonormal series covariance estimator is used, the critical values can be approximated very well by the quantiles of a noncentral F distribution. A simulation study shows that statistical tests based on the new fixed-smoothing approximation are much more accurate in size than existing tests.