跳到正文
收录说明
收录范围
保留计量方法研究、相关统计基础,以及实质涉及计量方法的应用、教程、比较和综述。书评、刊务及明确无关内容不公开;身份或相关性证据不足的记录保留待核,补充可靠材料后可重新判断。
当前期刊来源见下方官网链接及期刊目录。综合经济学与金融期刊逐篇筛查计量方向,证据不足时暂不公开。正刊、在线优先论文和已核实接收稿合并展示;按标题、可靠摘要、目录及身份匹配的出版社 JEL 分类核验相关性;计量类 JEL 与摘要相互印证,无 JEL 不直接排除。历史回补以可取得的注册题录和目录为准,不等于覆盖全部纸本。
日期与排序
默认按最近发表排序:正刊使用刊期,待刊使用上线日期;缺少上线日期时,排序参考登记或首次收录时间,并在卡片注明。更多筛选可选择出版状态和日期口径;卷期依据出版社元数据,不因待刊时间较长而推定已编入正刊。仅有月份或年份时,日期区间相交也会纳入。

日期精度:到日 0 篇 · 到月 18 篇 · 到年 0 篇

分类与引用
领域和方法依据题名、摘要初步分类,可人工修正;分类不代表论文质量。引用数采用 OpenAlex 单条关联记录,不合计版本。
更新与 AI 解读
正常题录、可靠摘要与引用同步继续;日、周、月简报按核实的发表与修订日期汇总,历史补录保留在文献库。AI 解读依据可靠摘要或已核实原文片段,已有解读保留供阅读参考;缺少可靠材料的条目保持待解读,浏览页面不会触发生成。
18 篇论文
导出

Bayesian quantile regression methods

贝叶斯分位数回归方法

Abstract This paper is a study of the application of Bayesian exponentially tilted empirical likelihood to inference about quantile regressions. In the case of simple quantiles we show the exact form for the likelihood implied by this method and compare it with the Bayesian bootstrap and with Jeffreys' method. For regression quantiles we derive the asymptotic form of the posterior density. We also examine Markov chain Monte Carlo simulations with a proposal density formed…

AI 解读阅读原文 ↗BibTeX ↓

Orthogonal Parameters and Panel Data

正交参数与面板数据

This paper describes a class of consistent estimators for short panels with fixed effects. The method is to find an orthogonal reparametrization of the fixed effects and then to integrate the new effects from the likelihood with respect to an appropriately chosen prior density. The resulting marginal posterior densities of the common parameters have modes that are shown to be consistent in the models examined here. The main result concerns the first-order autoregressive…

AI 解读阅读原文 ↗BibTeX ↓

Panel Data with Survival: Hospitalization of HIV-Positive Patients

带生存结局的面板数据:HIV 阳性患者住院分析

This article provides an analysis of the hospitalization experience of a panel of HIV-positive patients. It is part of a program of work designed to study the medical expenditures of such patients and their variation both between people and over time. We model the joint distribution of the inpatient episodes and the survival times of a panel of patients over 15 months. The model induces correlation between hospitalization and death via an unmeasured, person-specific, frailty…

AI 解读阅读原文 ↗BibTeX ↓

Exact Structural Inference in Optimal Job-Search Models

最优求职模型中的精确结构推断

This paper is a study of the exact posterior distributions of parameters in a stationary optimal job search model. The author exploits the simple latent structure of the search model when all job offers are observed to stimulate posterior distributions of structural parameters when the latent structure is imperfectly observed. These simulations enable him to show the unusual shape of the job search likelihood when the data are durations and accepted wages. The author also…

AI 解读阅读原文 ↗BibTeX ↓

An Econometric Analysis of Reservation Wages

保留工资的计量经济学分析

This paper describes an analysis of survey data on unemployed people who reported their asking wage and the wage they expected to earn. We show that if agents' behavior is described by the standard optimal job search model, one can use these data to deduce structural parameters rather than estimate them, and we report our deductions from two surveys^1 of British unemployed people.

AI 解读阅读原文 ↗BibTeX ↓

Grouping Estimators on Heteroscedastic Data

异方差数据中的分组估计量

This paper gives numerical comparisons of the efficiency of Ordinary Least Squares (OLS) and Grouping Estimators in simple linear regression. The disturbances are assumed to have unequal variances, and an assumption is made about the form of this heteroscedasticity. It is shown that for some types of heteroscedasticity a Grouping Estimator can be more efficient than Ordinary Least Squares.

AI 解读阅读原文 ↗BibTeX ↓

A Note on an ‘Errors in Variables’ Model

关于一个变量测量误差模型的札记

We consider an errors in variables model in which the ‘true’ part of the determining variable is generated by a simple forecasting mechanism. It is shown that the Least Squares errors in variables bias can be interpreted in terms of the parameters of the forecasting mechanism; and that the ‘standard’ result for this bias may no longer hold in this situation. An empirical illustration of the results is given.

AI 解读阅读原文 ↗BibTeX ↓