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Estimation in a class of nonlinear heteroscedastic time series models
Abstract: Parameter estimation in a class of heteroscedastic time series models is investigated. The existence of conditional least-squares and conditional likelihood estimators is proved. Their consistency and their asymptotic normality are established. Kernel estimators of the noise’s density and its derivatives are defined and shown to be uniformly consistent. A simulation experiment conduct...
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Godambe's (1985) theorem on optimal estimating eq'uation for stochasti; processes is applied to nonlinear time series estimation problems. Examples are considered from the usual classes of nonlinear time series models. Recursive estimation procedure based on optimal estimating equation is provided. It is also shown that prefiltered estimates can be used to obtain the optimal estimate from a non...
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prediction of hydrological variables is a highly effective tool in water resource management. One of the important tools for modeling hydrological processes is the use of time series modeling and analysis. River series production series can be used by time series models in various studies such as drought, flood, reservoir systems design and many other purposes For this purpose, monthly flow dat...
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This paper discusses nonparametric series estimation of integrable cointegration models using Hermite functions. We establish the uniform consistency and asymptotic normality of the series estimator. The Monte Carlo simulation results show that the performance of the estimator is numerically satisfactory. We then apply the estimator to estimate the stock return predictive function. The out–of–s...
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Well-known Box-Jenkins Autoregressive integrated moving average (ARIMA) methodology has virtually dominated analysis of time-series data since 1930s. However, it is applicable to only those data that are either stationary or can be made so. Another limitation is that the resultant model is “Linear”. During the last two decades or so, the area of “Nonlinear time-series” is rapidly growing. Here,...
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ژورنال
عنوان ژورنال: Stochastic Processes and their Applications
سال: 1986
ISSN: 0304-4149
DOI: 10.1016/0304-4149(86)90099-2