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支持向量决策方法AI 解读

The paper develops a support vector machine (SVM) for binary decision-making within a utility framework. Given an information set, a decision-maker first predicts a binary outcome and then selects a binary action based on this prediction to maximize expected utility, where the utility function can depend on the action taken, observable covariates, and the binary outcome subsequently realized. The proposed maximum utility SVM differs from the traditional SVM in four key…

内生二元处理的无条件政策效应：无条件 MTE 方法AI 解读

This paper studies the identification and estimation of policy effects when treatment status is binary and endogenous. We introduce a new class of marginal treatment effects (MTEs) based on the influence function of the functional underlying the policy target. We show that an unconditional policy effect can be represented as a weighted average of the newly defined MTEs over the individuals who are indifferent about their treatment status. We provide conditions for point…

一般政策干预的无条件效应AI 解读

This paper studies the unconditional effects of a general policy intervention, which includes location-scale shifts and simultaneous shifts as special cases. The location-scale shift is intended to study a counterfactual policy aimed at changing not only the mean or location of a covariate but also its dispersion or scale. The simultaneous shift refers to the situation where shifts in two or more covariates take place simultaneously. For example, a shift in one covariate is…

长期高频观测回归中的渐近 F 检验AI 解读

This paper proposes tests of linear hypotheses when the variables may be continuous-time processes with observations collected at a high sampling frequency over a long span. Utilizing series long run variance (LRV) estimation in place of the traditional kernel LRV estimation, we develop easy-to-implement and more accurate F tests in both stationary and nonstationary environments. The nonstationary environment accommodates exogenous regressors that are general…

不相关创新时间序列模型的简单渐近 F 分布综合诊断检验AI 解读

We propose a simple asymptotically F-distributed portmanteau test for diagnostically checking whether the innovations in a parametric time series model are uncorrelated while allowing them to exhibit higher-order dependence of unknown forms. A transform of sample residual autocovariances removing the influence of parameter estimation uncertainty makes the test simple. Further, by employing the orthonormal series variance estimator, a special sample autocovariances estimator…

异方差与自相关稳健推断中的检验最优核选择AI 解读

The paper investigates the optimal kernel choice in heteroskedasticity and autocorrelation robust tests based on the fixed-b asymptotics. In parallel with the optimality of the quadratic spectral kernel under the asymptotic mean squared error criterion of the point estimator of the long run variance as considered in Andrews (1991) , we show that the optimality of the quadratic spectral kernel continues to hold under the testing-oriented criterion of Sun, Philips and Jin…

可能弱识别下的渐近 F 检验AI 解读

This paper develops asymptotic F tests robust to weak identification and temporal dependence. The test statistics we focus on are modified versions of the S statistic of Stock and Wright (2000) and the K statistic of Kleibergen (2005). In the former case, the modification involves only a multiplicative degree-of-freedom adjustment, and the modified S statistic is asymptotically F distributed under fixed-smoothing asymptotics regardless of the strength of the model…

双重差分回归中简便可靠的渐近 t 检验AI 解读

We propose an asymptotically valid t test that uses Student’s t distribution as the reference distribution in a difference-in-differences regression. For the asymptotic variance estimation, we adopt the clustering-by-time approach to accommodate cross-sectional dependence. This approach often assumes the clusters to be independent across time, but we allow them to be temporally dependent. The proposed t test is based on a special heteroscedasticity and autocorrelation robust…

Summary This paper considers a moderately explosive AR(1) process where the autoregressive root approaches unity from the right at a certain rate. We first develop a test for the null of moderate explosiveness under independent and identically distributed errors. We show that the t statistic is asymptotically standard normal regardless of whether the true process is dominated by the stochastic moderately explosive trend or the deterministic nonlinear drift trend. This result…

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…

评论AI 解读

I would like to congratulate the authors (Lazarus, Lewis, Stock, and Watson, LLSW hereafter) for the timely and authoritative practical recommendations for heteroscedasticity and autocorrelation ro...

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…

时间序列核平滑预渐近推断的固定带宽视角AI 解读

This paper develops robust testing procedures for nonparametric kernel methods in the presence of temporal dependence of unknown forms. Based on the fixed-bandwidth asymptotic variance and the pre-asymptotic variance, we propose a heteroskedasticity and autocorrelation robust (HAR) variance estimator that achieves double robustness — it is asymptotically valid regardless of whether the temporal dependence is present or not, and whether the kernel smoothing bandwidth is held…

工具变量分位数回归的平滑估计方程AI 解读

The moment conditions or estimating equations for instrumental variables quantile regression involve the discontinuous indicator function. We instead use smoothed estimating equations (SEE), with bandwidth h . We show that the mean squared error (MSE) of the vector of the SEE is minimized for some h > 0, leading to smaller asymptotic MSE of the estimating equations and associated parameter estimators. The same MSE-optimal h also minimizes the higher-order type I error of a…

非线性面板固定效应估计的 Bootstrap 与 k 步 Bootstrap 偏差修正AI 解读

Because of the incidental parameters problem, the fixed effects maximum likelihood estimator in a nonlinear panel data model is in general inconsistent when the time series length T is short and fixed. Even if T approaches infinity but at a rate not faster than the cross sectional sample size n , the fixed effects estimator is still asymptotically biased. This paper proposes using the standard bootstrap and k -step bootstrap to correct the bias. We establish the asymptotic…

灵活的非参数条件独立检验AI 解读

This paper proposes a nonparametric test for conditional independence that is easy to implement, yet powerful in the sense that it is consistent and achieves n −1/2 local power. The test statistic is based on an estimator of the topological “distance” between restricted and unrestricted probability measures corresponding to conditional independence or its absence. The distance is evaluated using a family of Generically Comprehensively Revealing (GCR) functions, such as the…

The paper develops an asymptotically valid F-test that is robust to spatial autocorrelation in a GMM framework. The validity of the F-test is established under mild conditions that can accommodate a wide range of spatial processes. The proposed F-test is very easy to implement, as critical values are from a standard F-distribution. The F-test achieves triple robustness: it is asymptotically valid regardless of the spatial autocorrelation, the sampling region, and the…

两步广义矩估计框架下的固定平滑渐近理论AI 解读

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…

评述AI 解读

Müller applies the theory of optimal statistical testing to heteroscedasticity and autocorrelation robust (HAR) inference in the presence of strong autocorrelation. As a starting point, Müller uses...

In the presence of heteroscedasticity and autocorrelation of unknown forms, the covariance matrix of the parameter estimator is often estimated using a nonparametric kernel method that involves a lag truncation parameter. Depending on whether this lag truncation parameter is specified to grow at a slower rate than or the same rate as the sample size, we obtain two types of asymptotic approximations: the small-b asymptotics and the fixed-b asymptotics. Using techniques for…

可能误设的半非参数时间序列模型的筛推断AI 解读

This paper establishes the asymptotic normality of plug-in sieve M estimators of possibly irregular functionals of semi-nonparametric time series models. We show that, even when the sieve score process is not a martingale difference sequence, the asymptotic variance in the case of irregular functionals is the same as those for independent data. Using an orthonormal series long run variance estimator, we construct a “pre-asymptotic” Wald statistic and show that it is…

固定效应线性面板模型中对异方差与时空依赖稳健的推断AI 解读

This paper studies robust inference for linear panel models with fixed effects in the presence of heteroskedasticity and spatiotemporal dependence of unknown forms. We propose a bivariate kernel covariance estimator that nests existing estimators as special cases. Our estimator improves upon existing estimators in terms of robustness, efficiency, and adaptiveness. For distributional approximations, we considered two types of asymptotics: the increasing-smoothing asymptotics…

尺寸准确且具有功效的简便 GMM 过度识别检验AI 解读

Based on the series long run variance estimator, we propose a new class of over-identification tests that are robust to heteroscedasticity and autocorrelation of unknown forms. We show that when the number of terms used in the series long run variance estimator is fixed, the conventional J statistic, after a simple correction, is asymptotically F-distributed. We apply the idea of the F-approximation to the conventional kernel-based J tests. Simulations show that the J∗ tests…