跳到正文
← 返回论文列表与筛选
阅读说明

本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。

分类依据与纠错

AI 根据所提供的论文材料归类:文本明确处理多个截面单位的时间序列观测,并讨论截面异方差、序列相关和随机系数,故归入面板数据。

反馈分类问题

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Arthur Havenner
Ronald Herman

时间序列与截面数据的合并估计

研究问题
如何计算多个截面单位的时间序列数据的合并估计,并让程序适应不同的误差协方差结构与随机系数设定?
方法与思路
作者以矩矩阵计算最小二乘和工具变量估计,并提供处理异方差、序列相关、截面协方差及随机系数的合并估计程序。
材料陈述的贡献
摘要介绍可修改的计算模块与三类现成合并例程,支持用户按误差协方差假设构造程序;其所述贡献主要体现为计算实现。
阅读关注点
阅读时核对三类例程各自的协方差与随机系数设定、矩阵分解所需条件,以及迭代停止规则和适用的数据维度。
阅读边界
摘要属于程序说明,未给出估计量性质或应用结果;所述 FORTRAN 与 SPEAKEASY/FEDEASY 软件的当前可得性无法判断。

原文摘要

PROGRAMS ARE AVAILABLE to facilitate calculation of least squares and instrumental variable coefficients and related statistics from data consisting of time-series observations for each of several cross-sectional units.' The (FORTRAN) programs2 must be used with the SPEAKEASY/FEDEASY processor3 and take the form of three FEDEASY commands that (1) form a moment (cross product) matrix from an arbitrarily large number of observations (CPMATRIX), (2) calculate least squares and instrumental variable coefficients and attendant statistics for any subset of variables included in the moment matrix (CPOLSQ and CPINST), and (3) decompose a square matrix into two upper triangular factors by a Cholesky decomposition (DECOMPOSE). The presence of these programs in the FEDEASY vocabulary allows users to easily construct, using SPEAKEASY matrix commands, routines appropriate for their particular error covariance assumptions (e.g., hypotheses of nth order autoregression, block diagonality, etc.). In addition, three pooling routines are available both for immediate use and as a guide in coding special applications. The first (KMENTA), described in [2, pp. 508-512], as cross-sectionally heteroskedastic and time-wise autoregressive is a standard block diagonal covariance design that is computable for any number of years and cross-sections. The second (HAVENNER) emphasizes cross-sectional covariances by calculating a full covariance matrix between all cross-sections at each point in time, while assuming first-order autoregression of errors in the same cross-section.4 The third routine (SWAMY) assumes that the coefficients are random and essentially follows the specification in [3, p. 312], except that the equation errors of each cross-sectional unit are assumed first-order autoregressive.4 All three routines are written in the high-level SPEAKEASY/FEDEASY command language and are easily modified to suit particular cases. All have standard options such as residual and moment matrix printing, and the first two have iteration limits, convergence criterion overrides, and restart provisions. The programs are available on request with the SPEAKEASY/FEDEASY System from Stan Cohen, Speakeasy Center, Argonne National Laboratory, Argonne, Illinois 60439 or separately (if the user already has SPEAKEASY/FEDEASY installed) from Ronald Herman, Board of Governors of the Federal Reserve System, Washington, D.C. 20551.

笔记与分组
相关领域论文