The first order nonlinear autoregressive model ‎ ‎with Ornstein Uhlenbeck processes driven by white ‎noise

[1] Barndorff-Nielsen OE., Shephard, N., 2001, Non-Gaussian Ornstein-Uhlenbeck-
based models and some of their uses in  nancial economics, J R Stat Soc B 63,167{241.
[2] Chaiyapo, N., Phewchean, N., 2017, An application of Ornstein-Uhlenbeck pro-
cess to commodity pricing in Thailand, Advances in Difference Equations, 179,1-10.
[3] Delgado, M.A., Robinson, P.M., 1992, Nonparametric and semiparametric methods for economic research, J. Eco. Sur., 6, 201{249.
[4] Farnoosh, R., Mortazavi, S.J., 2011, A Semiparametric Method for Estimating nonlinear autoregressive model with dependent errors, Journal of NonlinearAnalysis, 74(17), 6358{6370.
[5] Hajrajabi, A., Fallah, A., 2017, Nonlinear semiparametric AR(1) model with skewsymmetric Innovations, Communications in Statistics Simulation and Computation, Taylor and Francis, 1-10.
[6] Hidalgo, F.J., 1992, Adaptive semiparametric estimation in the presence of autocorrelation of unknown form, J. Time Series Anal., 13, 47{78.
[7] Hjort, N.L., Jones, M.C., 1996, Locally parametric nonparametric density estimation, Ann. Statist., 24, 1619{1647.
[8] Tjostheim, D., 1994, Nonlinear time series: A selective review, J. Stat.
[9] Tsay, R. S., 2013, An Introduction to Analysis of Financial Data with R, NewJersey: John Wiley and Sons.
[10] Valdivieso, L., Schoutens, W., Tuerlinckx, F., 2009, Maximum likelihood estimation in processes of Ornstein Uhlenbeck type, Stat Infer Stoch Process, 1{19.
[11] Zhuoxi, Y. Dehui, W. and Ningzhoneg, S., 2009, Semiparametric estimation of regression function in autoregressive models, Journal of Statistics and Probability Letters, 79(2), 165-172.