scholarly journals Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

2019 ◽  
Vol 11 (2) ◽  
pp. 168781401881346
Author(s):  
Jorge Luis Tena García ◽  
Erasmo Cadenas Calderón ◽  
Gilberto González Ávalos ◽  
Eduardo Rangel Heras ◽  
Alain Mbikayi Tshikala
Keyword(s):  
Wind Turbine ◽  
Autoregressive Models ◽  
Output Energy ◽  
Daily Output ◽  
Nonlinear Autoregressive ◽  
Nonlinear Autoregressive Models

scholarly journals PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS

2011 ◽  
Vol 27 (6) ◽  
pp. 1236-1278 ◽  
Author(s):  
Mika Meitz ◽  
Pentti Saikkonen
Keyword(s):  
Asymptotic Normality ◽  
Autoregressive Models ◽  
Garch Models ◽  
Martingale Difference ◽  
First Order ◽  
First Results ◽  
Consistency And Asymptotic Normality ◽  
Quasi Maximum Likelihood ◽  
Nonlinear Autoregressive ◽  
Nonlinear Autoregressive Models

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first-order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale difference sequence. Strong consistency and asymptotic normality of the global Gaussian quasi-maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.

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