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存在样本流失时的矩估计AI 解读

We study the effects of the attrition of firms from longitudinal samples on the estimates of dynamic labor demand models. The reasons for attrition from business-based longitudinal samples are extremely varied and are related to both the economic activity of the business and the methods of acquiring sampling frame information for those businesses. We do an exhaustive study of the available information regarding the attrition of French firms from our analysis sample. We…

核平滑方法下的收益率曲线估计AI 解读

We introduce a new method for the estimation of discount functions, yield curves and forward curves from government issued coupon bonds. Our approach is nonparametric and does not assume a particular functional form for the discount function although we do show how to impose various restrictions in the estimation. Our method is based on kernel smoothing and is defined as the minimum of some localized population moment condition. The solution to the sample problem is not…

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理性成瘾的计量经济学AI 解读

This article reexamines the econometric estimation of rational-addiction models considered by Becker, Grossman, and Murphy (BGM) for cigarette consumption. The rational-addiction model poses a number of additional econometric difficulties including endogeneity due to the presence of leads and lags of the dependent variable and serial correlation in the disturbances. BGM considered a fixed-effects two-stage least squares (2SLS) estimator. It is well known that this estimator…

删失与截断回归模型中的条件矩限制AI 解读

Censored and truncated regression models with unknown distribution are important in econometrics. This paper characterizes the class of all conditional moment restrictions that lead to √ n -consistent estimators for these models. The semiparametric efficiency bound for each conditional moment restriction is derived. In the case of a nonzero bound it is shown how an estimator can be constructed and that an appropriately weighted version can attain the efficiency bound. These…

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广义矩条件下计算Γ极小极大决策规则的算法AI 解读

We present an algorithm for calculating a $\Gamma$-minimax decision rule, when is given by a finite number of generalized moment conditions. Such a decision rule minimizes the maximum of the integrals of the risk function with respect to all distributions in $\Gamma$. The inner maximization problem is approximated by a sequence of linear programs. This approximation is combined with an elimination technique which quickly reduces the domain of the variables of the outer…

动态面板样本选择模型的估计AI 解读

This paper considers the problem of identification and estimation in panel data sample selection models with a binary selection rule, when the latent equations contain strictly exogenous variables, lags of the dependent variables, and unobserved individual effects. We derive a set of conditional moment restrictions which are then exploited to construct two-step GMM-type estimators for the parameters of the main equation. In the first step, the unknown parameters of the…

含测量误差与工具变量的恩格尔曲线估计：贝叶斯分析AI 解读

In this article, we consider the Bayesian estimation of Engel curves specified as the Working–Leser form with the measurement errors on both the left and the right sides. It is noteworthy that in the Bayesian approach no additional variation data (i.e., instrumental variables) are required in contrast with the non-Bayesian approach. We present the Bayesian estimation procedure in both models without an instrumental variable and with an instrumental variable. We also compare…

M估计中标量参数剖面经验似然函数的近似AI 解读

Empirical likelihood possesses many of the important properties of genuine parametric likelihood, but it is computationally burdensome, especially when nuisance parameters are present. This paper presents two approximations to the profile empirical likelihood function for a scalar parameter of interest in the context of M‐estimation; the simpler approximation is based on the third derivative of the profile log empirical likelihood function at its maximising point, while the…

通过模型选择实现自回归或 β 混合回归中的自适应估计AI 解读

We study the problem of estimatingsome unknown regression function in a $\beta$-mixing dependent framework. To this end, we consider some collection of models which are finite dimensional spaces. A penalized least-squares estimator (PLSE) is built on a data driven selected model among this collection. We state non asymptotic risk bounds for this PLSE and give several examples where the procedure can be applied (autoregression, regression with arithmetically $\beta$-mixing…

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GMM 估计量的稳健推断AI 解读

The local robustness properties of generalized method of moments (GMM) estimators and of a broad class of GMM based tests are investigated in a unified framework. GMM statistics are shown to have bounded influence if and only if the function defining the orthogonality restrictions imposed on the underlying model is bounded. Since in many applications this function is unbounded, it is useful to have procedures that modify the starting orthogonality conditions in order to…

局部线性平滑的经验似然置信区间AI 解读

Empirical likelihood is considered in conjunction with the local linear smoother to construct confidence intervals for a nonparametric regression function with bounded support. The coverage error of the empirical likelihood confidence intervals is evaluated and is shown to be of the same order throughout the support of the regression function. This is a significant improvement over confidence intervals based directly on the asymptotic normal distribution of the local linear…

利用总体辅助信息的分层随机抽样经验似然推断AI 解读

The empirical likelihood method under stratified random sampling is used for making inferences on finite population parameters. Our results show that it can lead to efficient estimators by making effective use of auxiliary population information in the form of overall population totals or means ascertained from external sources. Empirical likelihood ratio confidence intervals for the population mean are also studied, and adjusted intervals with asymptotically correct…

将广义矩方法推广至连续统矩条件AI 解读

This paper proposes a version of the generalized method of moments procedure that handles both the case where the number of moment conditions is finite and the case where there is a continuum of moment conditions. Typically, the moment conditions are indexed by an index parameter that takes its values in an interval. The objective function to minimize is then the norm of the moment conditions in a Hilbert space. The estimator is shown to be consistent and asymptotically…

测量误差与投资和q之间的关系AI 解读

Many recent empirical investment studies have found that the investment of financially constrained firms responds strongly to cash flow. Paralleling these findings is the disappointing performance of the q theory of investment: even though marginal q should summarize the effects of all factors relevant to the investment decision, cash flow still matters. We examine whether this failure is due to error in measuring marginal q. Using measurement errorconsistent generalized…

序列依赖的广义谱检验AI 解读

Summary Two tests for serial dependence are proposed using a generalized spectral theory in combination with the empirical distribution function. The tests are generalizations of the Cramér-von Mises and Kolmogorov-Smirnov tests based on the standardized spectral distribution function. They do not involve the choice of a lag order, and they are consistent against all types of pairwise serial dependence, including those with zero autocorrelation. They also require no moment…

广义矩估计AI 解读

The generalized method of moments (GMM) estimation has emerged as providing a ready to use, flexible tool of application to a large number of econometric and economic models by relying on mild, plausible assumptions. The principal objective of this volume is to offer a complete presentation of the theory of GMM estimation as well as insights into the use of these methods in empirical studies. It is also designed to serve as a unified framework for teaching estimation theory…

弱识别下的广义矩估计AI 解读

This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak-identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo…

面板 Probit 中的异方差误设：GMM 与模拟最大似然的小样本比较AI 解读

This paper compares generalized method of moments (GMM) and simulated maximum likelihood (SML) approaches to the estimation of the panel probit model. Both techniques circumvent multiple integration of joint density functions without the need to restrict the error term variance-covariance matrix of the latent normal regression model. Particular attention is paid to a three-stage GMM estimator based on nonparametric estimation of the optimal instruments for given conditional…