论文检索

不可忽略缺失机制下线性混合效应模型的渐近偏差AI 解读

Summary In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation,…

时间安排不平衡的纵向数据分析AI 解读

Summary The paper considers modelling, estimating and diagnostically verifying the response process generating longitudinal data, with emphasis on association between repeated meas-ures from unbalanced longitudinal designs. Our model is based on separate specifications of the moments for the mean, standard deviation and correlation, with different components possibly sharing common parameters. We propose a general class of correlation structures that comprise random effects,…

均值回归测量误差对工资与就业纵向研究的影响AI 解读

This note examines the implications of mean-reverting mea-surement error for two influential literatures based on longitudinal survey data: (1) the literature on real wage variation over the business cycle and (2) the literature on intertemporal substitution in labor supply. Accounting for mean-reverting measurement error suggests that real wages may be even more procyclical than indicated by recent longitudinal studies. We also find that the instrumental variables estimator…

选择与企业存续：1825—1914年造船业的证据AI 解读

Several theories of firm performance can explain the well known observation that survival is positively related to age. However, a more mundane explanation-selection bias driven by variations in firm quality-may also underlie the phenomenon. This paper employs a 90 year plant-level panel data set on the U.S. iron and steel shipbuilding industry of the nineteenth and early twentieth centuries to discriminate between the explanations. The shipbuilding industry exhibits the…

非平衡时间序列数据的非参数推断AI 解读

This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data are unbalanced or incomplete. In this case, one can work with only the common sample, to which a standard HAC/bootstrap theory applies, but at the expense of throwing away data and perhaps losing efficiency. An alternative is to use some sort of imputation method, but this requires additional modeling assumptions, which we would rather avoid. We show how…

含不可观测异质性的动态非线性面板模型：初始条件问题的简便解法AI 解读

Abstract I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models. Rather than attempting to obtain the joint distribution of all outcomes of the endogenous variables, I propose finding the distribution conditional on the initial value (and the observed history of strictly exogenous explanatory variables). The approach is flexible, and results in simple estimation strategies for at least three…

复发事件过程与失效时间数据的联合建模及估计AI 解读

Recurrent event data are commonly encountered in longitudinal follow-up studies related to biomedical science, econometrics, reliability, and demography. In many studies, recurrent events serve as important measurements for evaluating disease progression, health deterioration, or insurance risk. When analyzing recurrent event data, an independent censoring condition is typically required for the construction of statistical methods. In some situations, however, the…

非平稳面板时间序列长期平均关系的估计AI 解读

This paper proposes a new class of estimators of the long-run average relationship in nonstationary panel time series. The estimators are based on the long-run average variance estimate using bandwidth equal to T. The new estimators include the pooled least squares estimator and the fixed effects estimator as special cases. It is shown that the new estimators are consistent and asymptotically normal under both the sequential limit, wherein T → ∞ followed by n → ∞, and the…

随机前沿模型的半参数贝叶斯推断AI 解读

In this paper we propose a semiparametric Bayesian framework for the analysis of stochastic frontiers and efficiency measurement. The distribution of inefficiencies is modelled nonparametrically through a Dirichlet process prior. We suggest prior distributions and implement a Bayesian analysis through an efficient Markov chain Monte Carlo sampler, which allows us to deal with practically relevant sample sizes. We also allow for the efficiency distribution to vary with firm…

面板二元变量与充分性：推广条件 LogitAI 解读

This paper extends the conditional logit approach (Rasch, Andersen, Chamberlain) used in panel data models of binary variables with correlated fixed effects and strictly exogenous regressors. In a two-period two-state model, necessary and sufficient conditions on the joint distribution function of the individual-and-period specific shocks are given such that the sum of individual binary variables across time is a sufficient statistic for the individual effect. By extending a…

认识教育回报的异质性AI 解读

Abstract Using data from the National Longitudinal Survey of Youth (NLSY) we introduce and estimate various Bayesian hierarchical models that investigate the nature of unobserved heterogeneity in returns to schooling. We consider a variety of possible forms for the heterogeneity, some motivated by previous theoretical and empirical work and some new ones, and let the data decide among the competing specifications. Empirical results indicate that heterogeneity is present in…

季度面板数据中的动态与季节性AI 解读

This article presents an empirical analysis of the dynamics of individual earnings using Spanish quarterly earnings. I propose a new econometric methodology to control for the seasonality in my dataset. Moreover, I apply this methodology to the study of earnings mobility in Spain, using transition probabilities among earnings quintiles for the period 1985–1996. I find substantial mobility from one year to the next; however, earnings changes are basically among adjacent…

摘要暂缺

摘要暂缺

A concise derivation of the Wallace and Hussain fixed effects transformation.

摘要暂缺

摘要暂缺

纵向数据半参数建模的新估计与模型选择程序AI 解读

Semiparametric regression models are very useful for longitudinal data analysis. The complexity of semiparametric models and the structure of longitudinal data pose new challenges to parametric inferences and model selection that frequently arise from longitudinal data analysis. In this article, two new approaches are proposed for estimating the regression coefficients in a semiparametric model. The asymptotic normality of the resulting estimators is established. An…

协变量缺失的非线性混合效应模型：精确与近似推断AI 解读

Nonlinear mixed-effects (NLME) models are popular in many longitudinal studies, including human immunodeficiency virus (HIV) viral dynamics, pharmacokinetic analyses, and studies of growth and decay. In practice, covariates in these studies often contain missing data, and so standard complete-data methods are not directly applicable. In this article we propose Monte Carlo parameter-expanded (PX)-EM algorithms for exact and approximate likelihood inferences for NLME models…

不规则时点重复测量中由工作相关模型构造无偏估计方程AI 解读

The method of generalized estimating equations (GEEs) has been criticized recently for a failure to protect against misspecification of working correlation models, which in some cases leads to loss of efficiency or infeasibility of solutions. However, the feasibility and efficiency of GEE methods can be enhanced considerably by using flexible families of working correlation models. We propose two ways of constructing unbiased estimating equations from general correlation…

时间扭曲随机曲线的函数凸平均与同步AI 解读

Data that can be best described as a sample of curves are now fairly common in science and engineering. When the dynamics of development, growth, or response over time are at issue, subjects or experimental units may experience events at different temporal paces. For functional data where trajectories may be individually time-transformed, it is usually inadequate to use commonly used sample statistics, such as the cross-sectional mean or median or the cross-sectional sample…

面板模型中未知形式序列相关的小波检验AI 解读

Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet-based tests for serial correlation of unknown form in the estimated residuals of a…

随访时间不规则且依赖结局的纵向数据分析AI 解读

Summary A frequent problem in longitudinal studies is that subjects may miss scheduled visits or be assessed at self-selected points in time. As a result, observed outcome data may be highly unbalanced and the availability of the data may be directly related to the outcome measure and/or some auxiliary factors that are associated with the outcome. If the follow-up visit and outcome processes are correlated, then marginal regression analyses will produce biased estimates.…

估计高等教育的社会回报：来自纵向与重复横截面数据的证据AI 解读

Economists have speculated for at least a century that the social return to education may exceed the private return. In this paper, I estimate spillovers from college education by comparing wages for otherwise similar individuals who work in cities with different shares of college graduates in the labor force. OLS estimates show a large positive relationship between the share of college graduates in a city and individual wages, over and above the private return to education.…