Spatial autocorrelation and clusters in modelling corporate bankruptcy of manufacturing firms
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The interest in the prediction of firms’ bankruptcy is increasing in the recent recession period 2008–2012, when, in Italy, the number of distressed manufacturing firms increased sharply. The most popular model applied by bankruptcy researchers is the logit model (logistic regression model). In the present paper we extend this classical model in two different ways, to take into account the spatial effects that can highly affect bankruptcy probability. We propose to apply the spatial Autologistic model and the Logit Regression Tree, with the aim to find evidence of spatial dependence and spatial heterogeneity in bankruptcy probability, of the manufacturing firms of Prato and Florence (Italy). Our application shows that spatial contagion effects are an important issue when modelling bankruptcy probability. Moreover, the application of the regression tree analysis shows the presence of three different clusters, with heterogeneous behaviours.
KeywordsDefault probability Autologistic model Heterogeneity Spatial dependence
JEL ClassificationC13 C21 C54 R12
This study was not funded by any Institution.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Bellone, F., Musso, P., Nesta, L., & Quéré, M. (2006). Productivity and market selection of French manufacturing firms in the Nineties. Revue de L’OFCE, 97(5), 319–349.Google Scholar
- Besag, J. (1972). Nearest-neighbour systems and the auto-logistic model for binary data. Journal of the Royal Statistical Society B, 34, 75–83.Google Scholar
- Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society B, 36, 192–236.Google Scholar
- Brealey, R. A., Myers, S. C., & Marcus, A. J. (2011). Fundamentals of corporate finance. International edition. New York: McGraw Hill Irwin.Google Scholar
- Cashin, P., & Dattagupta, R. (2008). The anatomy of banking crises. Washington DC: IMF WP.Google Scholar
- Cressie, N. (1993). Statistics for spatial data, revised edition. New York: Wiley.Google Scholar
- Ferretti, S., Filippone, A., & Micucci, G. (2016). Le imprese uscite dal mercato nel corso della crisi (p. 317). Rome: Banca d’Italia, Occasional Papers.Google Scholar
- Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Chichester: Wiley.Google Scholar
- Gissel, J. L., Giacomino, D., & Akers, M. D. (2007). A review of bankruptcy prediction studies: 1930–present. Milwaukee: Marquette University WP.Google Scholar
- Istat (2015) The new geography of the labor market areas. Istat, Simplicissimus Book Farm, RomeGoogle Scholar
- Jones, S., & Hensher, D. A. (Eds.). (2008). Advances in credit risk modelling and corporate bankruptcy prediction. Cambridge: Cambridge University Press.Google Scholar
- Loader, C. (1999). Local regression and likelihood. New York: Springer.Google Scholar
- Lopez-Garcia, P., Puente, S., & Gómez, Á. L. (2007). Firm productivity dynamics in Spain. Banco de España Working Paper No. 0739.Google Scholar
- Regione Toscana (2017). I distretti industriali toscani secondo i principali indicatori di contesto, Ufficio Regionale di Statistica, May 2017.Google Scholar
- Tokunaga, S., Kageyama, M., Akune, Y., & Nakamura, R. (2014). Empirical analysis of agglomeration economies in the japanese assembly-type manufacturing industry for 1985–2000: Using agglomeration and coagglomeration indices. Review of Urban and Regional Development Studies, 26(1), 57–79.CrossRefGoogle Scholar