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  • Statistics In Transition

 

Article | 06-July-2017

BAYESIAN INFERENCE FOR STATE SPACE MODEL WITH PANEL DATA

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.

Ranjita Pandey, Anoop Chaturvedi

Statistics in Transition New Series, Volume 17 , ISSUE 2, 211–219

Sampling Methods | 25-May-2018

A BAYESIAN INFERENCE OF MULTIPLE STRUCTURAL BREAKS IN MEAN AND ERROR VARIANCE IN PANEL AR (1) MODEL

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

Varun Agiwal, Jitendra Kumar, Dahud Kehinde Shangodoyin

Statistics in Transition New Series, Volume 19 , ISSUE 1, 7–23

Article | 22-July-2019

ESTIMATION OF ENERGY INTENSITY IN INDIAN IRON AND STEEL SECTOR: A PANEL DATA ANALYSIS

Anukriti Sharma, Hiranmoy Roy, Narendra Nath Dalei

Statistics in Transition New Series, Volume 20 , ISSUE 2, 107–121

Article | 06-July-2017

ON THE RELATIONSHIPS BETWEEN SMART GROWTH AND COHESION INDICATORS IN THE EU COUNTRIES

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.

Beata Bal-Domańska, Elżbieta Sobczak

Statistics in Transition New Series, Volume 17 , ISSUE 2, 249–264

Article | 22-July-2019

ECONOMIC GROWTH AND ITS DETERMINANTS: A CROSS-COUNTRY EVIDENCE

Adedayo A. Adepoju, Tayo P. Ogundunmade

Statistics in Transition New Series, Volume 20 , ISSUE 2, 69–84

Article | 05-September-2021

Trade potential under the SAFTA between India and other SAARC countries: the augmented gravity model approach

Vipin Sharma, Vinod Kumar

Statistics in Transition New Series, Volume 22 , ISSUE 3, 81–97

Research Article | 01-June-2017

APPLICATION OF RADIAL BASIS FUNCTION NEURAL NETWORK TO PREDICT EXCHANGE RATE WITH
FINANCIAL TIME SERIES

 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.

AI SUN, JUI-FANG CHANG

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 2, 308–326

Research Article | 03-March-2021

Measurement of enterprise mobility among size classes, taking into account business demography

A extensive body of literature is devoted to the production of mobility measures based on transition matrices. The applications often involve panel data, and yet the impact of demographic events on enterprise mobility is not considered. The article aims provide a definition of enterprise mobility, in terms of the capability to create or liquidate jobs. Moreover, some existing mobility measures are modified so that they also take into account newborn and exiting firms. The proposed index has all

Camilla Ferretti

Statistics in Transition New Series, Volume 22 , ISSUE 1, 55–73

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