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- W2736646479 abstract "Bilinear processes are highly flexible for modeling nonlinear and heavy-tailed features that are often exhibited by financial and environmental time series. However, intractable likelihood functions, together with the lack of verifiable conditions of invertibility and stationarity, except for very simple bilinear processes, constraint their use as models. The traditional methods of least squares and conditional maximum likelihood (CML) do not give satisfactory results, particularly for heavy-tailed series. This paper aims to show the advantages and drawbacks of using simulation-based estimation techniques for bilinear models. Approximate Bayesian Computation (ABC), as well as sequential Markov chain Monte Carlo (MCMC) methods are often applied with success as inferential tools for time series. Due to ease with which one can simulate bilinear processes, ABC is particularly a promising inferential alternative. We assess the viability of ABC as an inferential tool for the bilinear processes. The performance of the ABC algorithm for parameter estimation is assessed using several simulated samples from simple bilinear models. The results are compared with the parameter estimates obtained using the CML method." @default.
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- W2736646479 date "2015-12-04" @default.
- W2736646479 modified "2023-10-17" @default.
- W2736646479 title "Parameter Estimation of Bilinear Processes Using Approximate Bayesian Computation" @default.
- W2736646479 hasPublicationYear "2015" @default.
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