Analyzing the Full BINMA Time Series Process Using a Robust GQL Approach
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Naushad Mamode Khan
,
Yuvraj Sunecher
Abstract
We investigate a new bivariate-integer valued moving average time series process where the innovation series follow the bivariate Poisson assumption under stationary moments and constant cross-correlations. Furthermore, due to the complication involved in specifying the joint likelihood function, this paper considers a robust generalized quasi-likelihood approach to estimate the mean, serial and dependence parameters. Unlike previous estimation techniques such as the Generalized Least Squares, this estimation approach here involves a two-step Newton-Raphson iterative procedure where in the first step, the serial and cross correlations are estimated while in the second step, these dependence estimates are used to compute iteratively the vector of regression coefficients. The consistency of the estimates under this approach is checked through several simulation experiments under different combinations of low and high serial and cross-correlations.
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Artikel in diesem Heft
- Testing for Nonlinearity in Conditional Covariances
- Tail Behavior and Dependence Structure in the APARCH Model
- Analyzing the Full BINMA Time Series Process Using a Robust GQL Approach
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Artikel in diesem Heft
- Testing for Nonlinearity in Conditional Covariances
- Tail Behavior and Dependence Structure in the APARCH Model
- Analyzing the Full BINMA Time Series Process Using a Robust GQL Approach
- Do They Still Matter? – Impact of Fossil Fuels on Electricity Prices in the Light of Increased Renewable Generation
