Abstract
This paper proposes a non-stationary bivariate integer-valued moving average of order 1 (BINMA(1)) model where the respective innovations are marginal COM-Poisson and unrelated. As opposed to other such bivariate time series model, the dependence between the series in the above is constructed via the relation between the current series with survivor elements of the other series at the preceding time point. Under these assumptions, the BINMA(1) process is shown to accommodate different levels and combinations of over-, equi- and under-dispersion. Since under the non-stationary conditions, the joint likelihood function is hardly laborious to construct, a generalized quasi-likelihood (GQL) method of estimation is proposed to estimate the dynamic effects and dependence parameters. The asymptotic and consistency properties of the GQL estimators are also established. Monte-Carlo experiments and a real-life application to analyze intra-day stock transactions are presented to validate the proposed model and the estimation methodology.
Acknowledgement
The authors are very grateful to Prof. M. Ristic, Prof. K. Sellers and Prof. D. Karlis for their valuable comments and suggestions and the Stock Exchange of Mauritius to share the micro stock data upon confidentiality agreement.
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Articles in the same Issue
- Research Articles
- Local Lagged Adapted Generalized Method of Moments: An Innovative Estimation and Forecasting Approach and its Applications
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Articles in the same Issue
- Research Articles
- Local Lagged Adapted Generalized Method of Moments: An Innovative Estimation and Forecasting Approach and its Applications
- Modelling with Dispersed Bivariate Moving Average Processes
- A Neural Network Method for Nonlinear Time Series Analysis
- Finite-Sample Theory and Bias Correction of Maximum Likelihood Estimators in the EGARCH Model