Chow-Lin Methods in Spatial Mixed Models

Wolfgang Polasek (), Richard Philipp Sellner and Carlos Llano Verduras

Working Paper Series from The Rimini Centre for Economic Analysis

Abstract: Missing data in dynamic panel models occur quite often since detailed recording of the dependent variable is often not possible at all observation points in time and space. In this paper we develop classical and Bayesian methods to complete missing data in panel models. The Chow-Lin (1971) method is a classical method for completing dependent disaggregated data and is successfully applied in economics to disaggregate aggregated time series. We will extend the space-time panel model in a new way to include cross-sectional and spatially correlated data. The missing disaggregated data will be obtained either by point prediction or by a numerical (posterior) predictive density. Furthermore, we point out that the approach can be extended to more complex models, like ow data or systems of panel data. The panel Chow-Lin approach will be demonstrated with examples involving regional growth for Spanish regions.

Keywords: Space-time interpolation; Spatial panel econometrics; MCMC; Spatial Chow-Lin; missing regional data; Spanish provinces; MCMC; NUTS: nomenclature of territorial units for statistics (search for similar items in EconPapers)
JEL-codes: C11 C15 C52 E17 R12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
Date: 2010-01
References: Add references at CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://www.rcfea.org/RePEc/pdf/wp47_10.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:rim:rimwps:47_10

Access Statistics for this paper

More papers in Working Paper Series from The Rimini Centre for Economic Analysis
Contact information at EDIRC.
Series data maintained by Roberto Patuelli ().