Functional Statistical Time Series Analysis of the Dividend Policy of Spanish Companies

  1. Salmerón Gómez, Román
  2. Ruiz Medina, María Dolores
Revista:
Aestimatio: The IEB International Journal of Finance

ISSN: 2173-0164

Año de publicación: 2011

Número: 3

Páginas: 6

Tipo: Artículo

Otras publicaciones en: Aestimatio: The IEB International Journal of Finance

Resumen

A functional statistical analysis of a data panel constituted by 33 companies in the IBEX-35, Spain, during the period 2006 to 2009, is achieved for the investigation of possible changes in the dividend policy. Empirical evidence of positive functional correlation of dividend policy changes with future changes of earnings per share is provided by the data panel studied. The functional estimation of the dividend annual increment per share, in all the companies of the sample, is obtained by implementing a functional version of Kalman filtering algorithm in an Autoregressive Hilbertian (ARH(1)) process framework

Referencias bibliográficas

  • Abramovicha, F. and Angelini, C. (2006). Testing in mixed-effects FANOVA models, Journal of Statistical Planning and Inference, 136, pp. 4326-4348.
  • Anselin, L. (1988). Spatial Econometrics: Methods and Models, Kluwer Academic Publishers. Dordrecht.
  • Anselin, L. and Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics, in Ullah, A. and Giles, D. E. A. (Eds.), Handbook of Applied Economic Statistics, Marcel Dekker, New York.
  • Beck, T., Demirgüc-Kunt, A. and Maksimovic, V. (2008). Financing patterns around the world: Are small firms different?, Journal of Financial Economics, 89, pp. 467-487.
  • Bevan, A. A. and Danbolt, J. (2004). Testing for inconsistencies in the estimation of UK capital structure determinants, Applied Financial Economics, 14, pp. 55-66.
  • Bosq, D. (2000). Linear Processes in Function Spaces, Springer-Verlag, New York.
  • Bosq, D. and Blanke, D. (2007). Inference and Predictions in Large Dimensions, John Wiley and sons, Paris.
  • Cardot, H., Ferraty, F., Mas, A. and Sarda, P. (2006). Testing hypotheses in the functional linear model, Scandinavian Journal of Statistics, 30, pp. 241-255.
  • Cardot, H., Ferraty, F. and Sarda, P. (1999). Functional linear model, Statistics and Probability Letters, 45, pp. 11-22.
  • Cardot, H., Ferraty, F. and Sarda, P. (2003). Spline estimators for the functional linear model, Statistica Sinica, 13, pp. 571-591.
  • Cardot, H. and Sarda, P. (2005). Estimation in generalized linear model for functional data via penalized likelihood, Journal of Multivariate Analysis, 92, pp. 24-41.
  • Dautray, R. and Lions, J. L. (1992). Mathematical Analysis and Numerical Methods for Science and Technology 3, Spectral Theory and Applications, Springer-Verlag, Berlin.
  • Driscoll, J. and Kraay, A. (1998). Consistent covariance matrix estimation with spatially dependent panel data, Review of Economics and Statistics, 80, pp. 549-560.
  • Dunford, N. and Schwartz, J. T. (1971). Linear Operators, Part III, Spectral Operator, Wiley Interscience, New York.
  • Fan, J. and Zhang, J. T. (2000). Two-step estimation of functional linear models with applications to longitudinal data, Journal of the Royal Statistical Society: Series B, 62, pp. 303-322.
  • Ferraty, F. and Vieu, P. (2006). Nonparameric Functional Data Analysis, Springer, New York.
  • Giannetti, M. (2003). Do better institutions mitigate agency problems? Evidence from corporate finance choices, Journal of Financial and Quantitative Analysis, 38, pp. 185-212.
  • Grullon, G., Michaely, R. and Swaminathan, B. (2002). Are dividend changes a sign of firm maturity? Journal of Business, 75, pp. 387-424.
  • Hall, G. C., Hutchinson, P. J. and Michaelas, N. (2004). Determinants of the Capital Structures of European SMEs, Journal of Business Finance & Accounting, 31, pp. 711-728.
  • Healy, P. M. and Palepu, K. G. (1988). Earnings information conveyed by dividend initiations and omissions, Journal of Financial Economics, 21, pp. 149-175.
  • Hoover, D. R., Rice, J. A., Wu, C. O. and Yang, L. P. (1998). Nonparametric smoothing estimates of timevarying coefficient models with longitudinal data, Biometrika, 85, pp. 809-822.
  • James, G. M. (2002). Generalized linear models with functional predictors, Journal of the Royal Statistical Society: Series B, 64, pp. 411-432.
  • Jiménez, F. and Palacín, M. P. (2007). Determinantes de la estructura financiera de la empresa, Revista Europea de Dirección y Economía de la Empresa, 16, pp. 7-22.
  • Jong, A., Kabir, R. and Nguyen, T. T. (2008). Capital structure around the world: The roles of firmand country-specific determinants, Journal of Banking & Finance, 32, pp. 1954-1969.
  • La Rocca, M. L., La Rocca, T. L. and Cariola, A. (2010). The influence of local institutional differences on the capital structure of SMEs: Evidence from Italy, International Small Business Journal, 28, pp. 234-257.
  • López-Iturriaga, F. J. and Rodríguez-Sanz, J. A. (2008). Capital structure and institutional setting: a decompositional and international analysis, Applied Economics, 40, pp. 1851-1864.
  • Masry, E. (2005). Nonparametric regression estimation for dependent functional data: asymptotic normality, Stochastic Processes and their Applications, 115, pp. 155-177.
  • Palacin-Sánchez, M. J. and Di Pietro, F. (2011). El valor informativo de los dividendos en las empresas españolas. Revista Europea de Dirección y Economía de la Empresa, 20, pp. 9-22.
  • Psillaki, M. and Daskalakis, N. (2009). Are the determinants of capital structure country or firm specific?, Small Business Economics, 33, pp. 319-333.
  • Rachdi, M. and Vieu, P. (2007). Nonparametric regression for functional data: automatic smoothing parameter selection, Journal of Statistical Planning and Inference, 137, pp. 2784-2801.
  • Rajan, R. G. and Zingales, L. (1995). What Do We Know about Capital Structure? Some Evidence from International Data, The Journal of Finance, 50, pp. 1421-1460.
  • Ramsay, J. O. and Silverman, B.W. (2005). Functional Data Analysis, Springer.
  • Ruiz-Medina, M. D. (2011). Spatial autoregressive and moving average Hilbertian processes, Journal of Multivariate Analysis, 102, pp. 292-305.
  • Ruiz-Medina, M. D., Salmerón, R. and Angulo, J. M. (2007). Kalman filtering from POP-based diagonalization of ARH(1), Computational Statistics & Data Analysis, 51, pp. 4994-5008.
  • Ruiz-Medina, M. D. and Salmerón, R. (2010). Functional maximum-likelihood estimation of ARH(p) models, Stochastic Environmental Research and Risk Assessment, 24, pp. 131-146.
  • Salmerón, R. and Ruiz-Medina, M. D. (2009). Multispectral decomposition of FAR(p) models, Stochastic Environmental Research and Risk Assessment, 23, pp. 289-297.
  • Utrero-González, N. (2007). Banking regulation, institutional framework and capital structure: International evidence from industry data, The Quarterly Review of Economics and Finance, 47, pp. 481-506.
  • Wu, C. O., Chiang, C.T. and Hoover, D. R. (1998). Asymptotic confidence regions for kernel smoothing of a varying-coefficient model with longitudinal data, Journal of the American Statistical Association, 93, pp. 1388-1402.
  • Youa, J. and Zhoub, X. (2006). Statistical inference in a panel data semiparametric regression model with serially correlated errors, Journal of Multivariate Analysis, 97, pp. 844-873.
  • Ruiz-Medina, M. D. (2009). Functional denoising and reconstruction of fractal image sequences, Random Operators and Stochastic Equations, 17, pp. 275-293.