Seminar
Spatial Chow-Lin methods for data completion in econometric flow
models
Wolfgang Polasek
Faculdade de Ciencia da Universidade de Porto
E-mail: [email protected]
Abstract
Flow data across regions can be modeled by spatial econometric models, see LeSage and Pace (2009).
Recently, regional studies became interested in the aggregation and disaggregation of flow models,
because trade data cannot be obtained at a disaggregated level but data are published on an aggregate
level. Furthermore, missing data in disaggregated flow models occur quite often since detailed
measurements are often not possible at all observation points in time and space. In this paper we
develop classical and Bayesian methods to complete flow data. The Chow and Lin (1971) method was
developed for completing disaggregated incomplete time series data.
We will extend this method in a general framework to spatially correlated flow data using the crosssectional Chow-Lin method of Polasek et al. ( 2010). The missing disaggregated data can be obtained
either by feasible GLS prediction or by a Bayesian (posterior) predictive density.
References
Chow, G. C. and Lin, A., (1971) `Best linear unbiased interpolation, distribution and extrapolation of
time series by related series', Review of Economics and Statistics, 53 (1971), 372±5.
James Le Sage, and Robert K. Pace: Introduction to spatial econometrics. CRC Press Inc., 2009
W. Polasek, C. Llano und R. Sellner (2010) Bayesian methods for completing data in space-time panel
models, Review of Economic Analysis 2 (2010) 194–214.
Theme proposal
Chow-Lin forecasting of spatial cross-section and flow models by MCMC
Advisor(s): Wolfgang Polasek
Aims: To estimate SAR or SEM models with the program package R using MCMC methods. Use
the existing packages in CRAN and cross-section data (regional or medical) data. Find out if the
financial crisis has changed the spatial patterns. Apply the Chow-Lin method to estimate/predict
disaggregate data. Extend the model to time series or panel data and flow data. Find about the
right indicators for intensive and extensive variables.
References:
L. Anselin, Spatial Econometrics: Methods and Models, Kluwer Academic Publishers, The
Netherlands, 1988.