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局部单位根回归变量与未知序列依赖下的低频协整回归AI 解读

This article develops new t and F tests in a low-frequency transformed triangular cointegrating regression when one may not be certain that the economic variables are exact unit root processes. We first show that the low-frequency transformed and augmented OLS (TA-OLS) method exhibits an asymptotic bias term in its limiting distribution. As a result, the test for the cointegration vector can have substantially large size distortion, even with minor deviations from the unit…

时间序列两步 GMM 的有限样本校正推断AI 解读

This paper develops a finite-sample corrected inference for the efficient generalized method of moments (GMM) in time series. To capture a higher-order uncertainty embodied in estimating the time series GMM weight matrix, we extend the finite-sample corrected variance formula of Windmeijer (2005) to heteroskedasticity autocorrelated robust (HAR) inference. Using fixed-smoothing asymptotics, we show that our finite-sample corrected test statistics lead to standard asymptotic…

线性 GMM 的双重修正稳健方差估计量AI 解读

We propose a new finite sample corrected variance estimator for the linear generalized method of moments (GMM) including the one-step, two-step, and iterated estimators. Our formula also corrects the over-identification bias in variance estimation on top of the commonly used finite sample correction of Windmeijer (2005), which corrects the bias from estimating the efficient weight matrix, so is doubly corrected. An important feature of the proposed double correction is that…

简便且可靠的聚类稳健 GMM 推断AI 解读

This paper develops a new asymptotic theory for GMM estimation and inference in the presence of clustered dependence. The key feature of our alternative asymptotics is that the number of clusters G is regarded as fixed as the sample size increases. Under the fixed-G asymptotics, we show that the Wald and t tests in two-step GMM are asymptotically pivotal only if we recenter the estimated moment process in the clustered covariance estimator (CCE). Also, the J statistic, the…

是否应再进一步？GMM一步法与两步法的精确比较AI 解读

According to conventional asymptotic theory, the two-step generalized method of moments (GMM) estimator and test perform at least as well as the one-step estimator and test in large samples. The conventional asymptotic theory completely ignores the estimation uncertainty in the weighting matrix, and as a result it may not reflect finite-sample situations well. In this paper, we employ the more accurate fixed-smoothing asymptotic framework to compare the performances of the…

协整系统中简易、稳健且精确的 F 与 t 检验AI 解读

This paper proposes new, simple, and more accurate statistical tests in a cointegrated system that allows for endogenous regressors and serially dependent errors. The approach involves first transforming the time series using orthonormal basis functions in L 2 [0, 1], which have energy concentrated at low frequencies, and then running an augmented regression based on the transformed data and constructing the test statistics in the usual way. The approach is essentially the…

有效 GMM 框架下的渐近 F 与 t 检验AI 解读

This paper considers two-step efficient GMM estimation and inference where the weighting matrix and asymptotic variance matrix are based on the series long run variance estimator. We propose a simple and easy-to-implement modification to the trinity of test statistics in the two-step efficient GMM setting and show that the modified test statistics are all asymptotically F distributed under the so-called fixed-smoothing asymptotics. The modification is multiplicative and…