On the estimation problem in AR(1) model with exponential innovations

Document Type : Original Scientific Paper

Authors

1 Department of Statistics‎, ‎College of Science‎, ‎Payame Noor University‎, ‎P.O‎. ‎Box‎, ‎19395-4697‎, ‎Tehran‎, ‎Iran

2 Department of Statistics, College of Science, Shiraz University, Shiraz, Iran

Abstract

In this article, the autoregressive model of order one with exponential innovations is considered. The maximum likelihood and Bayes estimators of the autoregression parameter, under squared error loss function with non-informative prior are examined. A simulation study is conducted to compare the behavior of the estimators via their relative bias and risks. Moreover, a real data example is presented.

Keywords

Main Subjects


Anděl, J. (1988). On AR(1) process with exponential white noise. Communications in Statistics-Theory and Methods, 17(5):1481–1495.
Anděl, J. (1989). Non-negative autoregressive processes. Journal of Time Series Analysis, 10(1):1–11.
Anděl, J. (1992). Nonnegative multivariate AR(1) processes. Kybernetika, 28(3):213–226.
Anděl, J. (1998). An estimator for parameters of a nonlinear nonnegative multidimensional AR(1) process. Applications of Mathematics, 43(5):389–398.
Anděl, J. and Garrido, M. (1991). Bayesian analysis of non-negative AR(2) processes. Statistics, 22(4):579–588.
Bell, C.B. and Smith, E.P. (1986). Inference for non-negative autoregressive schemes. Communications in Statistics-Theory and Methods, 15(8):2267–2293.
Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.
Brockwell, P.J. and Marquardt, T. (2003). Fractional Integration of Continuous-time ARMA Processes. Technical report of Technische Universität München.
Brockwell, P.J. and Marquardt, T. (2005). Levy-driven and fractionally integrated ARMA processes with continuous time parameter. Statistica Sinica, 15:477–494.
Datta, S. and McCormick, W.P. (1995). Bootstrap inference for a first-order autoregression with positive innovations. Journal of the American Statistical Association, Theory and Methods, 90(432):1289–1300.
Gaver, D.P. and Lewis, P.A.W. (1980). First-order autoregressive gamma sequences and point processes. Advances in Applied Probability, 12:27–745.
Ibazizen, M. and Fellag, H. (2003). Bayesian estimation of an AR(1) process with exponential white noise. Statistics, 37(5):365–372.
Larbi, L. and Fellag, H. (2016). Robust Bayesian analysis of an autoregressive model with exponential innovations. Afrika Statistika, 11(1):955–964.
Nielsen, B. and Shephard, N. (2001). Non-Gaussian Ornstein-Uhlenbeck based models and some of their uses in financial economics (with discussion). Journal of the Royal Statistical Society, Series B, 63(2):167–241.
Nielsen, B. and Shephard, N. (2003). Likelihood analysis of a first-order autoregressive model with exponential innovations. Journal of Time Series Analysis, 24(3):337–344.
Saadatmand, A., Nematollahi, A.R. and Sadooghi-Alvandi, S.M. (2017). On the estimation of missing values in AR(1) model with exponential innovations. Communications in Statistics-Theory and Methods, 46(7):3393–3400.
Tsai, H. and Chan, K.S. (2005). A note on non-negative continuous time processes. Journal of the Royal Statistical Society, Series B, 67(4):589–597.
Tsai, H. and Chan, K.S. (2007). A note on non-negative ARMA processes. Journal of Time Series Analysis, 28(3):350–360.
Turkmann, M.A.A. (1990). Bayesian analysis of an autoregressive process with exponential white noise. Statistics, 21(4):601–608.