Multicollinearity and financial constraint in investment decisions: a bayesian generalized ridge regression
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
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Data(s) |
15/07/2015
15/07/2015
2011
|
Resumo |
This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification. |
Formato |
287-299 |
Identificador |
Journal of Applied Statistics, v. 38, p. 287-299, 2011. 0266-4763 http://hdl.handle.net/11449/125311 http://dx.doi.org/10.1080/02664760903406462 5089831236213689 7788895623440612 7555125918098797 |
Idioma(s) |
eng |
Relação |
Journal of Applied Statistics |
Direitos |
closedAccess |
Palavras-Chave | #Investment decision #Financial constraint #Bayesian ridge regression #Bayesian approach #Capital intensity |
Tipo |
info:eu-repo/semantics/article |