Bayesian Methods for Completing Data in Spatial Models

IF 0.7 Q3 ECONOMICS Review of Economic Analysis Pub Date : 2010-08-14 DOI:10.15353/rea.v2i2.1472
W. Polasek, Carlos Llano, Richard Sellner
{"title":"Bayesian Methods for Completing Data in Spatial Models","authors":"W. Polasek, Carlos Llano, Richard Sellner","doi":"10.15353/rea.v2i2.1472","DOIUrl":null,"url":null,"abstract":"Chow and Lin (1971) were the first to develop a unified framework for the three problems(interpolation, extrapolation and distribution) of predicting times series by related series(the ‘indicators’). This paper develops a spatial Chow-Lin procedure for cross-sectional data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a spatial context and derive the BLUE for ML and Bayesian MCMC estimation. In an example, we apply the procedure to Spanish regional GDP data between2000 and 2004. We assume that only NUTS-2 GDP is known and predict GDP at NUTS-3level by using socio-economic and spatial information available at NUTS-3. The spatial neighbourhood is defined by either km distance, travel time, contiguity or trade relationships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted values with the observed ones.","PeriodicalId":42350,"journal":{"name":"Review of Economic Analysis","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2010-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Review of Economic Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15353/rea.v2i2.1472","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 23

Abstract

Chow and Lin (1971) were the first to develop a unified framework for the three problems(interpolation, extrapolation and distribution) of predicting times series by related series(the ‘indicators’). This paper develops a spatial Chow-Lin procedure for cross-sectional data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a spatial context and derive the BLUE for ML and Bayesian MCMC estimation. In an example, we apply the procedure to Spanish regional GDP data between2000 and 2004. We assume that only NUTS-2 GDP is known and predict GDP at NUTS-3level by using socio-economic and spatial information available at NUTS-3. The spatial neighbourhood is defined by either km distance, travel time, contiguity or trade relationships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted values with the observed ones.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
空间模型中数据补全的贝叶斯方法
Chow和Lin(1971)首先针对相关序列(“指标”)预测时间序列的三个问题(内插、外推和分布)提出了统一的框架。本文提出了一种用于截面数据的空间周-林方法,并对经典估计方法和贝叶斯估计方法进行了比较。我们概述了空间背景下的误差协方差结构,并推导了ML和贝叶斯MCMC估计的BLUE。在一个示例中,我们将该程序应用于2000年至2004年之间的西班牙地区GDP数据。我们假设只有NUTS-2的GDP是已知的,并利用NUTS-3的社会经济和空间信息预测NUTS-3水平的GDP。空间邻域由公里距离、旅行时间、邻近度或贸易关系来定义。在进行敏感性分析后,提出了预报精度标准,并将预测值与实测值进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
1.10
自引率
0.00%
发文量
10
审稿时长
26 weeks
期刊最新文献
The Nexus between Causal Macroeconomic Relations in Japan Foreign Direct Investment and the Robustness of Host-Country Commitment The (non) impact of education on marital dissolution Demand for Money in Greece After Euro Area and Policy Uncertainties Ethnic Inequality and Anti-authoritarianism in Sub-Saharan Africa
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1