Double smoothing local linear estimation in nonlinear time series

References

• Ballini, R., I. Luna, L. M. D. Lima, R. Lanna, and F. Silveira. 1995. A comparative analysis of neurofuzzy, ann and arima models for brazilian stock index forecasting. SCE - Computing in Economics and Finance.
   Google Scholar
• Chen, R., and R. S. Tsay. 1993. Nonlinear additive ARX models. Journal of the American Statistical Association 88 (423):955–67. doi:10.1080/01621459.1993.10476363.
   Web of Science ®Google Scholar
• Dedecker, J., P. Doukhan, G. Lang, J. R. Leon, S. Louhichi, and C. Prieur. 2007. Weak dependence: With examples and applications. New York, NY: Springer.
   Google Scholar
• Doukhan, P. 1994. Mixing-properties and example. New York, NY: Springer-Verlag.
   Google Scholar
• Fan, J. 1993. Local linear regression smoothers and their minimax efficiencies. Annals of Statistics 21 (1):196–216.
   Web of Science ®Google Scholar
• Fan, J., and I. Gijbels. 1996. Local polynomial modelling and its applications, Volume 66 of Monographs on statistics and applied probability. London: Chapman & Hall.
   Google Scholar
• Fan, J., and Q. Yao. 1998. Efficient estimation of conditional variance functions in stochastic regression. Biometrika 85 (3):645–60. doi:10.1093/biomet/85.3.645.
   Web of Science ®Google Scholar
• Franke, J., J. Kreiss, and E. Mammen. 2002. Bootstrap of kernel smoothing in nonlinear time series. Bernoulli 8 (1):1–37.
   Web of Science ®Google Scholar
• Hall, P., and C. C. Heyde. 1980. Martingale Limit theory and its applications. New York: Academic Press.
   Google Scholar
• Härdle, W. 1990. Applied nonparametric regression. Cambridge, UK: Cambridge University Press.
   Google Scholar
• He, H., and L. S. Huang. 2009. Double-smoothing for bias reduction in local linear regression. Journal of Statistical Planning and Inference 139 (3):1056–72. doi:10.1016/j.jspi.2008.06.011.
   Web of Science ®Google Scholar
• Herrmann, R. 1986. Better price forecasts with international buffer stocks? A box/jenkins approach for the 1980 international cocoa agreement. Studies in Economics and Finance 10 (1):34–59. doi:10.1108/eb028662.
   Google Scholar
• He, H., W. Tang, and G. Zuo. 2014. Statistical inference in the partial linear models with the double smoothing local linear regression method. Journal of Statistical Planning and Inference 146:102–12. doi:10.1016/j.jspi.2013.10.004.
   Web of Science ®Google Scholar
• Hossain, M. F., and S. Rahman. 2006. Weak-form efficiency: Testimony of Dhaka stock exchange. Journal of Business Research 8:1–12.
   Google Scholar
• Jarrett, J. E., and E. Kyper. 2011. Arima modeling with intervention to forecast and analyze Chinese stock prices. International Journal of Engineering Business Management 3 (3):17–58. doi:10.5772/50938.
   Google Scholar
• Jones, D. A. 1978. Nonlinear autoregressive processes. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 360 (1700):71–95.
   Google Scholar
• Koch, P. D., and T. W. Koch. 1994. Forecasting stock returns in the Japanese, UK and US markets during the crash of October 1987. Managerial Finance 20 (2):68–89. doi:10.1108/eb018464.
   Google Scholar
• Masry, E. 1997. Local polynomial estimation of regression functions for mixing processes. Scandinavian Journal of Statistics 24 (2):165–79. doi:10.1111/1467-9469.00056.
   Web of Science ®Google Scholar
• Rosenblatt, M. 1956. Remarks on some nonparametric estimates of a density function. The Annals of Mathematical Statistics 27 (3):832–37. doi:10.1214/aoms/1177728190.
   Google Scholar
• Ruppert, D., and M. P. Wand. 1994. Multivariate locally weighted least squares regression. Annals of Statistics 22 (3):1346–70.
   Web of Science ®Google Scholar
• Tang, W., G. Zuo, and H. He. 2011. Double-smoothing for varying coefficient models. Journal of Nonparametric Statistics 23 (4):917–26. doi:10.1080/10485252.2011.588707.
   PubMed Web of Science ®Google Scholar
• Tong, H. 1990. Nonlinear time series: A dynamical system approach. London: Oxford University Press.
   Google Scholar
• Wong, C. C., M. Chan, and C. Lam. 2000. Financial time series forecasting by neural network using conjugate gradient learning algorithm and multiple linear regression weight initialization. Computing in Economics and Finance 61, Society for Computational Economics.
   Google Scholar