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联立方程Rao-Yu模型下人均消费支出的小区域估计

Small area estimation of consumption per capita expenditure under simultaneous equation Rao-Yu model.

作者信息

Noviyanti Reny Ari, Rumiati Agnes Tuti

机构信息

Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.

BPS, Statistics of Sumatera Utara Province, Medan 20123, Indonesia.

出版信息

MethodsX. 2024 Dec 16;14:103083. doi: 10.1016/j.mex.2024.103083. eCollection 2025 Jun.

Abstract

This article proposed a simultaneous equation model for small area estimation with random area and time-varying effects called the Simultaneous Equation Rao-Yu (SERY) model. In the context of small area estimation, many socioeconomic variables are likely to exhibit not only correlations but also causal relationships. Therefore, it is considered to use simultaneous equation model for indirect estimation method in a small-area. The SERY model was developed to accommodate causal relationships among the variables of interest in time series and cross-sectional data. The SERY model is a modification of the Rao-Yu model, which was constructed using simultaneous equation that allows endogenous variables as explanatory variables. For fitting linear mixed models, three-stage least squares restricted maximum likelihood method was proposed to derive the empirical best linear unbiased predictor and mean squared error estimator. Finally, the model was applied to estimate consumption per capita expenditure of food and non-food. Some highlights of the proposed method are:•We presented the SERY model, a small area estimation model, which was constructed using simultaneous equation to accommodate causal relationships among the variables of interest.•The parameter estimation method used three-stage least squares restricted maximum likelihood.•Applied to estimate consumption per capita expenditure of food and non-food.

摘要

本文提出了一种用于小区域估计的联立方程模型,该模型具有随机区域和时变效应,称为联立方程饶-于(SERY)模型。在小区域估计的背景下,许多社会经济变量不仅可能表现出相关性,还可能存在因果关系。因此,考虑在小区域中使用联立方程模型作为间接估计方法。SERY模型的开发是为了适应时间序列和横截面数据中感兴趣变量之间的因果关系。SERY模型是饶-于模型的一种改进,饶-于模型是使用允许内生变量作为解释变量的联立方程构建的。为了拟合线性混合模型,提出了三阶段最小二乘约束最大似然法来推导经验最佳线性无偏预测器和均方误差估计器。最后,将该模型应用于估计食品和非食品的人均消费支出。所提方法的一些亮点包括:

•我们提出了SERY模型,这是一种小区域估计模型,它使用联立方程构建,以适应感兴趣变量之间的因果关系。

•参数估计方法使用三阶段最小二乘约束最大似然法。

•应用于估计食品和非食品的人均消费支出。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/627d/11718344/f0cf9007cf1e/ga1.jpg

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