References
- Agresti, A. (2002), Categorical Data Analysis (Wiley Series in Probability and Statistics), New York: Wiley-Interscience.
- Anderson, H. M., and Vahid, F. (2005), “Nonlinear Correlograms and Partial Autocorrelograms,” Oxford Bulletin of Economics and Statistics, 67, 957–982.
- Bagnato, L., De Capitani, L., Mazza, A., and Punzo, A. (2015), “SDD: An R Package for Serial Dependence Diagrams,” Journal of Statistical Software, 64, 1–19.
- Bagnato, L., De Capitani, L., and Punzo, A. (2014), “Testing Serial Independence via Density-Based Measures of Divergence,” Methodology and Computing in Applied Probability, 16, 627–641.
- Bagnato, L., and Punzo, A. (2010), “On the Use of χ2-Test to Check Serial Independence,” Statistica & Applicazioni, VIII, 57–74.
- Bagnato, L., Punzo, A., and Nicolis, O. (2012), “The Autodependogram: A Graphical Device to Investigate Serial Dependences,” Journal of Time Series Analysis, 33, 233–254.
- Bellman, R. (1961), Adaptive Control Process, Princeton, NJ: Princeton University Press.
- Bollerslev, T., Chou, R., and Kroner, K. (1992), “ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence,” Journal of Econometrics, 52, 5–59.
- Cochran, W. G. (1954), “Some Methods for Strengthening the Common χ2 Tests,” Biometrics, 10, 417–451.
- Diks, C. (2009), “Nonparametric Tests for Independence,” in Encyclopedia of Complexity and Systems Science, ed. R. A. Meyers, New York: Springer, pp. 6252–6271.
- Genest, C., and Rémillard, B. (2004), “Test of Independence and Randomness Based on the Empirical Copula Process,” Test, 13, 335–369.
- Hall, P., and Wolff, R. (1995), “On the Strength of Dependence of a Time Series Generated by a Chaotic Map,” Journal of Time Series Analysis, 16, 571–583.
- Hallin, M., and Mélard, G. (1988), “Rank-Based Tests for Randomness Against First-Order Serial Dependence,” Journal of the American Statistical Association, 83, 1117–1128.
- Hastie, T. (2013), gam: Generalized Additive Models, R package, Version 1.12, available at http://CRAN.R-project.org/package=gam.
- Hochberg, Y. (1988), “A Sharper Bonferroni Procedure for Multiple Tests of Significance,” Biometrika, 75, 800–802.
- Holm, S. (1979), “A Simple Sequentially Rejective Multiple Test Procedure,” Scandinavian Journal of Statistics, 6, 65–70.
- Hommel, G. (1988), “A Stagewise Rejective Multiple Test Procedure Based on a Modified Bonferroni Test,” Biometrika, 75, 383– 386.
- King, M. (1987), “Testing for Autocorrelation in Linear Regression Models: A Survey,” in Specification Analysis in the Linear Model, eds. M. L. King, and D. E. A. Giles, London: Routledge Kegan & Paul, pp. 19–73.
- Ljung, G. M., and Box, G. E. P. (1978), “On a Measure of Lack of Fit in Time Series Models,” Biometrika, 65, 297–303.
- Mann, H. B., and Wald, A. (1942), “On the Choice of the Number of Class Intervals in the Application of the Chi Square Test,” The Annals of Mathematical Statistics, 13, 306–317.
- Rao, T. S. (1981), “On the Theory of Bilinear Time Series Models,” Journal of the Royal Statistical Society, Series B, 43, 244–255.
- R Core Team (2015), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing.
- Robinson, P. M. (1991), “Consistent Nonparametric Entropy-Based Testing,” The Review of Economic Studies, 58, 437–453.
- Romano, J. P., and Thombs, L. A. (1996), “Inference for Autocorrelations Under Weak Assumptions,” Journal of the American Statistical Association, 91, 590–600.
- Simes, R. J. (1986), “An Improved Bonferroni Procedure for Multiple Tests of Significance,” Biometrika, 73, 751–754.
- Skaug, H. J., and Tjøstheim, D. (1993), “A Nonparametric Test of Serial Independence Based on the Empirical Distribution Function,” Biometrika, 80, 591–602.
- Trapletti, A., Hornik, K., and LeBaron, B. (2015), tseries: Time Series Analysis and Computational Finance, R package, Version 0.10-34, available at http://CRAN.R-project.org/package=tseries.
- Verbeek, M. (2000), A Guide to Modern Econometrics, New York: Wiley.
- Wright, S. P. (1992), “Adjusted p-Values for Simultaneous Inference,” Biometrics, 48, 1005–1013.
- Wuertz, D., and Chalabi, Y. (2013), fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling, R package, Version 3010.82, available at http://CRAN.R-project.org/package=fgarch.
- Yang, L., Härdle, W., and Nielsen, J. P. (1999), “Nonparametric Autoregression With Multiplicative Volatility and Additive Mean,” Journal of Time Series Analysis, 20, 579–604.
- Zhou, Z. (2012), “Measuring Nonlinear Dependence in Time-Series, a Distance Correlation Approach,” Journal of Time Series Analysis, 33, 438–457.