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Markov random‐field models for estimating local labour markets

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  • Maria Rita Sebastiani

Abstract

Summary. This work is motivated by data on daily travel‐to‐work flows observed between pairs of elemental territorial units of an Italian region. The data were collected during the 1991 population census. The aim of the analysis is to partition the region into local labour markets. We present a new method for this which is inspired by the Bayesian texture segmentation approach. We introduce a novel Markov random‐field model for the distribution of the variables that label the local labour markets for each territorial unit. Inference is performed by means of Markov chain Monte Carlo methods. The issue of model hyperparameter estimation is also addressed. We compare the results with those obtained by applying a classical method. The methodology can be applied with minor modifications to other data sets.

Suggested Citation

  • Maria Rita Sebastiani, 2003. "Markov random‐field models for estimating local labour markets," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(2), pages 201-211, May.
    • Handle: RePEc:bla:jorssc:v:52:y:2003:i:2:p:201-211
    DOI: 10.1111/1467-9876.00398
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    Cited by:

    1. Paolo Postiglione & M. Andreano & Roberto Benedetti, 2013. "Using Constrained Optimization for the Identification of Convergence Clubs," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 151-174, August.
    2. Roberto Benedetti & Monica Pratesi & Nicola Salvati, 2013. "Local stationarity in small area estimation models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 81-95, March.
    3. Domenica Panzera & Paolo Postiglione, 2014. "Economic growth in Italian NUTS 3 provinces," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 273-293, August.

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