- Box, G. E. P., Jenkins, G. M. (1970). Time Series Analysis Forecasting and Control, San Francisco: Holden-Day.
- Chu, C. K., Marron, J. S. (1991). Choosing a kernel regression estimator. Statistical Science 6:404–436.
- Diebold, F. X., Gunther, T. A., Tay, A. S. (1998). Evaluating density forecasts with applications to financial risk management. International Economic Review 39:863–883.
- Dudewicz, E. J., Van Der Meulen, E. C. (1981). Entropy-based tests of uniformity. Journal of the American Statistical Association 76:967–974.
- Fernandes, M., Neri, B. (2010). Nonparametric entropy-based tests of independence between stochastic processes. Econometric Reviews 29:276–306.
- Gasser, T., Müller, H. G. (1979). Kernel estimation of regression functions. In: Smoothing Techniques for Curve Estimation. Lecture Notes in Mathematics, Heidelberg, Vol. 757, pp. 23–68.
- Ghosh, B. K., Huang, W. M. (1991). The power and optimal kernel of the Bickel-Rosenblatt test for goodness of fit. Annals of Statistics 19:999–1009.
- Gokhale, D. V. (1983). On entropy-based goodness-of-fit tests. Computational Statistics & Data Analysis 1:157–165.
- Granger, C. W. J., Lin, J. L. (1994). Using the mutual information coefficient to identify lags in nonlinear models. Journal of Time Series Analysis 15:371–384.
- Granger, C. W., Maasoumi, E., Racine, J. (2004). A dependence metric for possibly nonlinear processes. Journal of Time Series Analysis 25:649–669.
- Hansen, B. E. (1994). Autoregressive conditional density estimation. International Economic Review 35:705–730.
- Hong, Y., White, H. (2005). Asymptotic distribution theory for nonparametric entropy measures of serial dependence. Econometrica 73:837–901.
- Hong, Y., Li, H. (2005). Nonparametric specification testing for continuous-time models with applications to term structure of interest rates. Review of Financial Studies 18:37–84.
- Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical Review 106:620.
- Joe, H. (1989). Relative entropy measures of multivariate dependence. Journal of the American Statistical Association 84:157–164.
- Joe, H. (1989). Estimation of entropy and other functionals of a multivariate density. Annals of the Institute of Statistical Mathematics 41:683–697.
- Jondeau, E., Rockinger, M. (2003). Conditional volatility, skewness, and kurtosis: Existence, persistence, and comovements. Journal of Economic Dynamics and Control 27:1699–1737.
- Kullback, S., Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics 22:79–86.
- Kullback, S. (1959). Information Theory and Statistics. New York: John Wiley & Sons.
- Lee, S. W., Hansen, B. E. (1994). Asymptotic theory for the GARCH(1,1) quasi-maximum likelihood estimator. Econometric Theory 10:29–52.
- Maasoumi, E. (1993). A compendium to information theory in economics and econometrics. Econometric Reviews 12: 137–181.
- Maasoumi, E., Racine, J. (2002). Entropy and predictability of stock market returns. Journal of Econometrics 107:291–312.
- Mack, Y. P., Müller, H. G. (1989). Convolution type estimators for nonparametric regression estimation. Statistics & Probability Letters 7:229–239.
- Matilla-Garcia, M., Ruiz Marin, M. (2008). A non-parametric independence test using permutation entropy. Journal of Econometrics 144:139–155.
- Matilla-Garcia, M., Ruiz Marin, M. (2009). Detection of non-linear structure in time series. Economics Letters 105:1–6.
- Priestley, M. B. (1988). Non-linear and Non-stationary Time Series Analysis. New York: Academic Press.
- Racine, J. S., Maasoumi, E. (2007). A versatile and robust metric entropy test of time-reversibility, and other hypotheses. Journal of Econometrics 138:547–567.
- Robinson, P. M. (1991). Consistent nonparametric entropy-based testing. Review of Economic Studies 58:437–453.
- Rice, J. (1984). Boundary Modification for Kernel Regression. Communications in Statistics 13:893–900.
- Shannon, C. E. (1948). The mathematical theory of communication. Bell Systerm Technical Journal July–Oct. Urbana: University of Illinois Press.
- Skaug, H. J., Tjøstheim, D. (1993). Nonparametric tests of serial independence. In: Subba Rao, T., eds. Developments in Time Series Analysis: The Priestley Birthday Volume. London: Chapman & Hall, pp. 207–229.
- Skaug, H. J., Tjøstheim, D. (1996). Measures of distance between Densities with application to testing for serial independence. In: Robinson, P., Rosenblatt, M., eds. Time Series Analysis in Memory of Hannan E.J.. New York: Springer-Verlag, pp. 363–377.
- Tsallis, C. (1988). Possible generalization of Boltzmann-Gibbs statistics. Journal of Statistical Physics 52:479–487.
- Ullah, A. (1996). Entropy, divergence and distance measures with econometric applications. Journal of Statistical Planning and Inference 49:137–162.
- Vasicek, O. (1976). A test for normality based on sample entropy. Journal of the Royal Statistical Society, Series B (Methodological) 38:54–59.
- White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica 50:1–25.
- Wiener, N. (1949). Time Series. Cambridge: MIT press.
- Zheng, J. X. (2000). A consistent test of conditional parametric distributions. Econometric Theory 16:667–691.
An efficient integrated nonparametric entropy estimator of serial dependence
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