Estimation in nonlinear time series models

Stochastic Processes and their Applications

Volumn 21, Issue 2, 1986, Pages 251-273

  Tjøstheim, Dag ^a,b  

^a UNIVERSITY OF BERGEN   (Norway)

^b UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

asymptotic normality; conditional least square; consistency; maximum likelihood; nonlinear time series

Indexed keywords

EID: 46149141205     PISSN: 03044149     EISSN: None     Source Type: Journal    
DOI: 10.1016/0304-4149(86)90099-2     Document Type: Article

References (24)

1. Recursive estimation in non-linear time series models of autoregressive type
2. Autoregressive series with random parameters
3. The Lindeberg-Levy theorem for martingales
4. On the existence of strictly stationary solutions to bilinear equations
5. Une condition d'ergodicité pour des modelès bilinéaires à temps discret
6. Modelling nonlinear random vibrations using an amplitude-dependent autoregressive time series model
7. Bayesian forecasting (with discussion)
8. Non-linear autoregressive processes
9. On conditional least squares estimation for stochastic processes
10. Recursire estimation and forecasting in ARIMA models with time varying coefficients
11. Random Coefficient Autoregressive Models: An Introduction
12. Non-linear time series models for non-linear random vibrations
13. State-dependent models A general approach to time series analysis
14. The estimation of a nonlinear moving average model
15. An Introduction to Bispectral Analysis and Bilinear Time Series Models
16. Estimation in nonlinear time series models I: Stationary Series
17. Estimation in nonlinear time series models II: Some nonstationary series
18. Recent developments in nonlinear time series modelling
19. Some doubly stochastic time series models
20. Threshold Models in Nonlinear Time Series Analysis
21. Threshold autoregression, limit cycles and cyclical data (with discussion)
22. Sufficient conditions for ergodicity and recurrence of Markov chains on a general state space