Bayesian Methods for Completing Data in Spatial Models
Entity
UAM. Departamento de Análisis Económico, Teoría Económica e Historia EconómicaPublisher
Rimini Centre for Economic AnalysisDate
2010Citation
Review of Economic Analysis 2 (2010): 194–214ISSN
1973-3909Funded by
This paper is part of a project funded by the Jubilaeumsfonds of the Austrian National Bank (OeNB).Subjects
Interpolation; Spatial econometrics; MCMC; Spatial Chow-Lin; Missing regional data; Spatial autoregression; Forecasting by MCMC; NUTS; Estadística y Demografía / EstadísticaAbstract
Completing data sets that are collected in heterogeneous units is a quite frequent problem.
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 between
2000 and 2004. We assume that only NUTS-2 GDP is known and predict GDP at NUTS-3
level by using socio-economic and spatial information available at NUTS-3. The spatial
neighborhood 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.
Files in this item
Google Scholar:Llano Verduras, Carlos
-
Polasek, Wolfgang
-
Sellner, Richard
This item appears in the following Collection(s)
Related items
Showing items related by title, author, creator and subject.