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Financial distress, financial constraint and investment decision: Evidence from Brazil

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  • Bassetto, Camila F.
  • Kalatzis, Aquiles E.G.
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    Abstract

    This paper analyses the presence of financial constraint in the investment decisions of 367 Brazilian firms from 1997 to 2004, using a Bayesian econometric model with group-varying parameters. The motivation for this paper is the use of clustering techniques to group firms in a totally endogenous form. In order to classify the firms we used a hybrid clustering method, that is, hierarchical and non-hierarchical clustering techniques jointly. To estimate the parameters a Bayesian approach was considered. Prior distributions were assumed for the parameters, classifying the model in random or fixed effects. Ordinate predictive density criterion was used to select the model providing a better prediction. We tested thirty models and the better prediction considers the presence of 2 groups in the sample, assuming the fixed effect model with a Student t distribution with 20 degrees of freedom for the error. The results indicate robustness in the identification of financial constraint when the firms are classified by the clustering techniques.

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    Bibliographic Info

    Article provided by Elsevier in its journal Economic Modelling.

    Volume (Year): 28 (2011)
    Issue (Month): 1-2 (January)
    Pages: 264-271

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    Handle: RePEc:eee:ecmode:v:28:y:2011:i:1-2:p:264-271

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    Web page: http://www.elsevier.com/locate/inca/30411

    Related research

    Keywords: Bayesian econometrics Clustering techniques Financial constraint Investment decision;

    References

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    Cited by:
    1. Crisóstomo, Vicente Lima & López-Iturriaga, Félix Javier & Vallelado González, Eleuterio, 2014. "Nonfinancial companies as large shareholders alleviate financial constraints of Brazilian firm," Emerging Markets Review, Elsevier, vol. 18(C), pages 62-77.
    2. Saltari, E. & Travaglini, G., 2012. "A note on optimal capital stock and financing constraints," Economic Modelling, Elsevier, vol. 29(4), pages 1177-1180.
    3. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.

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