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Bayesian Methods for Completing Data in Space-Time Panel Models

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  • Carlos Llano

    (Universidad Autonoma de Madrid, Spain)

  • Wolfgang Polasek

    (Institute for Advanced Studies, Vienna, Austria and The Rimini Centre for Economic Analysis, Italy)

  • Richard Sellner

    (Institute for Advanced Studies, Vienna, Austria)

Abstract

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 and panel data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a spatial context and derive the BLUE for the ML and Bayesian MCMC estimation. Finally, we apply the procedure to Spanish regional GDP data between 2000-2004. 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 and trade relationships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted values with the observed ones.

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File URL: http://www.rcfea.org/RePEc/pdf/wp05_09.pdf
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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 05_09.

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Date of creation: Jan 2009
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Handle: RePEc:rim:rimwps:05_09

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Keywords: Interpolation; Spatial panel econometrics; MCMC; Spatial;

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