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迭代估计方程：线性收敛与渐近性质AI 解读

We propose an iterative estimating equations procedure for analysis of longitudinal data. We show that, under very mild conditions, the probability that the procedure converges at an exponential rate tends to one as the sample size increases to infinity. Furthermore, we show that the limiting estimator is consistent and asymptotically efficient, as expected. The method applies to semiparametric regression models with unspecified covariances among the observations. In the…

含协变量面板计数数据的两种基于似然的半参数估计方法AI 解读

We consider estimation in a particular semiparametric regression model for the mean of a counting process with “panel count” data. The basic model assumption is that the conditional mean function of the counting process is of the form E{ℕ(t)|Z}=exp(β0TZ)Λ0(t) where Z is a vector of covariates and Λ0 is the baseline mean function. The “panel count” observation scheme involves observation of the counting process ℕ for an individual at a random number K of random time points;…

固定效应动态面板离散选择模型的估计AI 解读

In this paper, I consider the estimation of dynamic binary choice panel data models with fixed effects. I use a Modified Maximum Likelihood Estimator (MMLE) that reduces the order of the bias in the Maximum Likelihood Estimator from O(T^(-1)) to O(T^(-2)), without increasing the asymptotic variance. I evaluate its performance in finite samples where T is not large, using Monte Carlo simulations. In Probit and Logit models containing lags of the endogenous variable and…

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动态面板中分数型因变量的估计及其在企业股利政策中的应用AI 解读

Fractional dependent variables and models with state dependence arise in many economic applications; however, estimating models with fractional dependent variables is complicated by the presence of two corner solution outcomes. When coupled with a dynamic panel data setting, estimating quantities of interest can be quite complex or computationally difficult. This article demonstrates a method for estimating fractional response variables, which is easy to implement, and…

长面板协整模型的统计推断AI 解读

This article presents a general likelihood-based framework for inference in panel vector autoregressive (VAR) models with cointegration restrictions. The cointegrating relationships are restricted to each cross section while the rest of the model is unrestricted. The homogeneous restriction of common cointegrating space is also considered. Asymptotic distributions of parameter estimators and the test statistics for the cointegrating rank and the homogeneous restriction are…

删失数据半参数回归模型中的极大似然估计AI 解读

Summary Semiparametric regression models play a central role in formulating the effects of covariates on potentially censored failure times and in the joint modelling of incomplete repeated measures and failure times in longitudinal studies. The presence of infinite dimensional parameters poses considerable theoretical and computational challenges in the statistical analysis of such models. We present several classes of semiparametric regression models, which extend the…

纵向数据的函数聚类与子结构识别AI 解读

Summary A functional clustering (FC) method, k-centres FC, for longitudinal data is proposed. The k-centres FC approach accounts for both the means and the modes of variation differentials between clusters by predicting cluster membership with a reclassification step. The cluster membership predictions are based on a non-parametric random-effect model of the truncated Karhunen–Loève expansion, coupled with a non-parametric iterative mean and covariance updating scheme. We…

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结果依赖型随访的纵向研究AI 解读

We propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual depends on the history of both observed longitudinal outcomes and previous measurement times. We begin with the investigation of the simplifying assumptions of Lipsitz, Fitzmaurice, Ibrahim, Gelber, and Lipshultz, and present a new model for analyzing such data by allowing…

正态误差成分 Logit 混合模型的参数识别AI 解读

Abstract Although the basic structure of logit‐mixture models is well understood, important identification and normalization issues often get overlooked. This paper addresses issues related to the identification of parameters in logit‐mixture models containing normally distributed error components associated with alternatives or nests of alternatives ( normal error component logit mixture , or NECLM, models). NECLM models include special cases such as unrestricted, fixed…

纵向数据的经验似然半参数回归分析AI 解读

A semiparametric regression model for longitudinal data is considered. The empirical likelihood method is used to estimate the regression coefficients and the baseline function, and to construct confidence regions and intervals. It is proved that the maximum empirical likelihood estimator of the regression coefficients achieves asymptotic efficiency and the estimator of the baseline function attains asymptotic normality when a bias correction is made. Two calibrated…

利用层级似然处理缺失数据问题AI 解读

Most statistical solutions to the problem of statistical inference with missing data involve integration or expectation. This can be done in many ways: directly or indirectly, analytically or numerically, deterministically or stochastically. Missing-data problems can be formulated in terms of latent random variables, so that hierarchical likelihood methods of Lee & Nelder (1996) can be applied to missing-value problems to provide one solution to the problem of integration of…

不可忽略非单调无应答下重复结果均值回归的估计AI 解读

We propose a new class of models for making inference about the mean of a vector of repeated outcomes when the outcome vector is incompletely observed in some study units and missingness is nonmonotone. Each model in our class is indexed by a set of unidentified selection-bias functions which quantify the residual association of the outcome at each occasion t and the probability that this outcome is missing after adjusting for variables observed prior to time t and for the…

利用单调多项式样条估计面板计数数据的均值函数AI 解读

We study nonparametric likelihood-based estimators of the mean function of counting processes with panel count data using monotone polynomial splines. The generalized Rosen algorithm, proposed by Zhang & Jamshidian (2004), is used to compute the estimators. We show that the proposed spline likelihood-based estimators are consistent and that their rate of convergence can be faster than n-super-1/3. Simulation studies with moderate samples show that the estimators have smaller…

纵向与空间数据相关函数的非参数估计及结肠致癌实验应用AI 解读

In longitudinal and spatial studies, observations often demonstrate strong correlations that are stationary in time or distance lags, and the times or locations of these data being sampled may not be homogeneous. We propose a nonparametric estimator of the correlation function in such data, using kernel methods. We develop a pointwise asymptotic normal distribution for the proposed estimator, when the number of subjects is fixed and the number of vectors or functions within…

外资收购对就业与工资的影响：来自芬兰生产单位的证据AI 解读

This paper examines the effect of foreign acquisition on wages and employment of different skill groups using panel data on Finnish establishments for 1988-2001. Exploiting the availability of a rich set of preacquisition controls, we use various regression and propensity-score matching methods. The results indicate that foreign acquisition has a positive effect on wages. The magnitude of this effect increases with the level of schooling of the workers. The wage increase is…

含测量误差的受限因变量模型的两阶段估计AI 解读

This paper deals with censored or truncated regression models where the explanatory variables are measured with additive errors. We propose a two-stage estimation procedure that combines the instrumental variable method and the minimum distance estimation. This approach produces consistent and asymptotically normally distributed estimators for model parameters. When the predictor and instrumental variables are normally distributed, we also propose a maximum likelihood based…

面板数据模型的选择校正：女性工资方程的应用AI 解读

In recent years a number of panel estimators have been suggested for sample selection models, where both the selection equation and the equation of interest contain individual effects which are correlated with the explanatory variables. Not many studies exist that use these methods in practise. We present and compare alternative estimators, and apply them to a typical problem in applied econometrics: the estimation of the wage returns to experience for females. We discuss…

利用劳动者流动测量企业动态AI 解读

Information on firm dynamics is critical to understanding economic activity, yet is fundamentally difficult to measure. In this article we introduce a new way of capturing dynamics: following clusters of workers as they move across administrative entities. We show that a worker flow approach improves linkages across firms in longitudinal business databases. The approach also provides conceptual insights into the changing structure of businesses and employer–employee…