The present work explores panel data set-up in a Bayesian state space model. The conditional posterior densities of parameters are utilized to determine the marginal posterior densities using the Gibbs sampler. An efficient one step ahead predictive density mechanism is developed to further the state of art in prediction-based decision making.
Statistics in Transition New Series , ISSUE 2, 211–219
This paper explores the effect of multiple structural breaks to estimate the parameters and test the unit root hypothesis in panel data time series model under Bayesian perspective. These breaks are present in both mean and error variance at the same time point. We obtain Bayes estimates for different loss function using conditional posterior distribution, which is not coming in a closed form, and this is approximately explained by Gibbs sampling. For hypothesis testing, posterior odds ratio is
Dahud Kehinde Shangodoyin
Statistics in Transition New Series , ISSUE 1, 7–23
Narendra Nath Dalei
Statistics in Transition New Series , ISSUE 2, 107–121
the well-being and prosperity of individuals. Economic cohesion is defined by the level of GDP per capita in PPS. Observation of these three phenomena forms the basis for the construction of panel data models and undertaking the assessment of the relationships between smart growth and economic and social cohesion factors. The study was performed on the group of 27 European Union countries in the period of 2002-2011.
Statistics in Transition New Series , ISSUE 2, 249–264
Adedayo A. Adepoju,
Tayo P. Ogundunmade
Statistics in Transition New Series , ISSUE 2, 69–84
presented to it. The model learning algorithm uses a diverse data set for training so as to adapt itself quickly for new exchange rate data. We apply the RBFNN to panel data of the exchange rates (USD/EUR, JPN/USD, USD/GBP, USD/CHY) are examined and optimized to be used for time-series predictions, some experiments testified the proposed method is effective and feasible.
International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 308–326