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部分不稳定广义矩模型中的有效推断AI 解读

This paper considers time series Generalized Method of Moments (GMM) models where a subset of the parameters are time varying. We focus on an empirically relevant case with moderately large instabilities, which are well approximated by a local asymptotic embedding that does not allow the instability to be detected with certainty, even in the limit. We show that for many forms of the instability and a large class of GMM models, usual GMM inference on the subset of stable…

劳动力市场项目评价中的序贯因果模型AI 解读

This article reviews inverse selection probability weighting to estimate dynamic causal effects. A distribution theory based on sequential generalized method of moments estimation is proposed and the method is applied to a reevaluation of some parts of the Swiss active labor market policy to obtain new results and discuss several issues about the implementation of the estimation procedure.

近弱工具变量下的有效 GMMAI 解读

This paper is in the line of the recent literature on weak instruments, which, following the seminal approach of Stock and Wright captures weak identification by drifting population moment conditions. In contrast with most of the existing literature, we do not specify a priori which parameters are strongly or weakly identified. We rather consider that weakness should be related specifically to instruments, or more generally to the moment conditions. In addition, we focus…

大量弱矩条件下的广义矩估计AI 解读

Using many moment conditions can improve efficiency but makes the usual generalized method of moments (GMM) inferences inaccurate. Two-step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias, but the usual standard errors are too small in instrumental variable settings. In this paper we give a new variance estimator for GEL that addresses this problem. It is consistent under the usual asymptotics and, under many weak moment asymptotics, is larger than…

具有异方差误差的神经网络需求系统AI 解读

In this paper we consider estimation of demand systems with flexible functional forms, allowing an error term with a general conditional heteroskedasticity function that depends on observed covariates, such as demographic variables. We propose a general model that can be estimated either by quasi-maximum likelihood (in the case of exogenous regressors) or generalized method of moments (GMM) if the covariates are endogenous. The specification proposed in the paper nests…

半参数多指标模型的设定与估计AI 解读

We propose an easy to use derivative-based two-step estimation procedure for semiparametric index models, where the number of indexes is not known a priori. In the first step various functionals involving the derivatives of the unknown function are estimated using nonparametric kernel estimators, in particular the average outer product of the gradient (AOPG). By testing the rank of the AOPG we determine the required number of indexes. Subsequently, we estimate the index…

相关数据中信息性相关结构选择的模型诊断检验AI 解读

In the generalized method of moments approach to longitudinal data analysis, unbiased estimating functions can be constructed to incorporate both the marginal mean and the correlation structure of the data. Increasing the number of parameters in the correlation structure corresponds to increasing the number of estimating functions. Thus, building a correlation model is equivalent to selecting estimating functions. This paper proposes a chi-squared test to choose informative…

随机条件持续期模型的替代估计方法AI 解读

This paper examines the estimation of the Stochastic Conditional Duration model by the empirical characteristic function and the generalized method of moments when maximum likelihood is unavailable. The joint characteristic function for the durations along with general expressions for the moments are derived, leading naturally to estimation via the empirical characteristic function and generalized method of moments. In a Monte Carlo study as well as an empirical application,…

矩条件冗余与似不相关回归模型中 OLS 的效率AI 解读

In this note, based on the generalized method of moments (GMM) interpretation of the usual ordinary least squares (OLS) and feasible generalized least squares (FGLS) estimators of seemingly unrelated regressions (SUR) models, we show that the OLS estimator is asymptotically as efficient as the FGLS estimator if and only if the cross-equation orthogonality condition is redundant given the within-equation orthogonality condition. Using the condition for redundancy of moment…

变系数动态面板模型的非参数估计AI 解读

We suggest using a class of semiparametric dynamic panel data models to capture individual variations in panel data. The model assumes linearity in some continuous/discrete variables that can be exogenous/endogenous and allows for nonlinearity in other weakly exogenous variables. We propose a nonparametric generalized method of moments (NPGMM) procedure to estimate the functional coefficients, and we establish the consistency and asymptotic normality of the resulting…

利用替代数据改进半参数估计AI 解读

Summary The paper considers estimating a parameter β that defines an estimating function U(y, x, β) for an outcome variable y and its covariate x when the outcome is missing in some of the observations. We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The efficiency of existing estimators for β depends critically on correctly specifying the conditional expectation of U given the surrogate and the covariate.…

线性面板数据模型的联合序列相关检验AI 解读

This paper proposes a joint error serial correlation test to be applied to linear panel data models after generalised method of moments estimation. This new test is an alternative inferential tool to both the m2 test of [Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277-297] and the overidentifying restrictions test. The proposed test, called the test,…

前测后测研究中基于经验似然的处理效应估计AI 解读

The pretest-posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest-posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that…

缺失数据下的估计方程推断AI 解读

There is a large and growing body of literature on estimating equation (EE) as an estimation approach. One basic property of EE that has been universally adopted in practice is that of unbiasedness, and there are deep conceptual reasons why unbiasedness is a desirable EE characteristic. This article deals with inference from EEs when data are missing at random. The investigation is motivated by the observation that direct imputation of missing data in EEs generally leads to…

新凯恩斯主义菲利普斯曲线的识别AI 解读

Abstract Phillips curves are central to discussions of inflation dynamics and monetary policy. The hybrid new Keynesian Phillips curve (NKPC) describes how past inflation, expected future inflation, and a measure of real aggregate demand drive the current inflation rate. This paper studies the (potential) weak identification of the NKPC under Generalized Method of Moments and traces this syndrome to a lack of higher‐order dynamics in exogenous variables. We employ analytic…

不可观测因素引致样本流失时的面板模型推断AI 解读

This paper concerns identification and estimation of a finite-dimensional parameter in a panel data-model under nonignorable sample attrition. Attrition can depend on second period variables which are unobserved for the attritors but an independent refreshment sample from the marginal distribution of the second period values is available. This paper shows that under a quasi-separability assumption, the model implies a set of conditional moment restrictions where the moments…

近奇异设计下的 GMM 与广义经验似然估计AI 解读

Nearly-Singular design relaxes the nonsingularity assumption of the limit weight matrix in GMM, and the nonsingularity of the limit variance matrix for the first order conditions in GEL. The sample versions of these matrices are nonsingular, but in large samples we assume these sample matrices converge to a singular matrix. This can result in size distortions for the overidentifying restrictions test and large bias for the estimators. This nearly-singular design may occur…

高阶矩不依赖解释变量时的非正态高效估计：残差增广最小二乘AI 解读

Under normality, least squares is efficient. However, if the errors are not normal, we can gain efficiency from the assertion that higher moments do not depend on the regressors. In this paper, we show how the assumption that higher moments do not depend on the regressors can be exploited in a GMM framework, and we provide simple estimators that are asymptotically equivalent to the GMM estimators. These estimators can be calculated by linear regressions which have been…

离散及随机采样扩散过程的 Hansen–Scheinkman 矩估计量分析AI 解读

We derive closed-form expansions for the asymptotic distribution of Hansen and Scheinkman [1995. Back to the future: generating moment implications for continuous-time Markov processes. Econometrica 63, 767-804] moment estimators for discretely, and possibly randomly, sampled diffusions. This result makes it possible to select optimal moment conditions as well as to assess the efficiency of the resulting parameter estimators relative to likelihood-based estimators, or to an…

基于经验似然的双重差分估计量AI 解读

Summary Recently there has been a surge in econometric and epidemiologic works focusing on estimating average treatment effects under various sets of assumptions. Estimation of average treatment effects in observational studies often requires adjustment for differences in pretreatment variables. Rosenbaum and Rubin have proposed the propensity score method for estimating the average treatment effect by adjusting pretreatment variables. In this paper, the empirical likelihood…

纳入总体层面信息的广义线性模型：基于经验似然的方法AI 解读

Summary In many situations information from a sample of individuals can be supplemented by population level information on the relationship between a dependent variable and explanatory variables. Inclusion of the population level information can reduce bias and increase the efficiency of the parameter estimates. Population level information can be incorporated via constraints on functions of the model parameters. In general the constraints are non-linear, making the task of…

使用辅助数据的 GMM 模型半参数效率AI 解读

We study semiparametric efficiency bounds and efficient estimation of parameters defined through general moment restrictions with missing data. Identification relies on auxiliary data containing information about the distribution of the missing variables conditional on proxy variables that are observed in both the primary and the auxiliary database, when such distribution is common to the two data sets. The auxiliary sample can be independent of the primary sample, or can be…

误设下线性理性预期模型估计量的偏差AI 解读

Most rational expectations models involve equations in which the dependent variable is a function of its lags and its expected future value. We investigate the asymptotic bias of generalized method of moment (GMM) and maximum likelihood (ML) estimators in such models under misspecification. We consider several misspecifications, and focus more specifically on the case of omitted dynamics in the dependent variable. In a stylized DGP, we derive analytically the asymptotic…

资产收益的马尔可夫切换多重分形模型AI 解读

Multifractal processes have recently been proposed as a new formalism for modeling the time series of returns in finance. The major attraction of these processes is their ability to generate various degrees of long memory in different powers of returns—a feature that has been found in virtually all financial data. Initial difficulties stemming from nonstationarity and the combinatorial nature of the original model have been overcome by the introduction of an iterative…