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In this paper alternative approaches for testing the unit root hypothesis in panel data are considered. First, a robust version of the Dickey‐Fuller t‐statistic under contemporaneous correlated errors is suggested. Second, the GLS t‐statistic is considered, which is based on the t‐statistic of the transformed model. The asymptotic power of both tests is compared against a sequence of local alternatives. To adjust for short‐run serial correlation of the errors, we propose a pre‐whitening procedure that yields a test statistic with a standard normal limiting distribution as N and T tends to infinity. The test procedure is further generalized to accommodate individual specific intercepts or linear time trends. From our Monte Carlo simulations it turns out that the robust OLS t‐statistic performs well with respect to size and power, whereas the GLS t‐statistic may suffer from severe size distortions in small and moderate sample sizes. The tests are applied to test for a unit root in real exchange rates.

Introduction/purpose: The significance of studying voting behaviour is underscored by its ability to gauge the continuity or divergence of electoral politics from historical trends, elucidating the real impact of the transformative ballot box, and contributing to the examination of democracy as a value among both masses and elites. Additionally, it aids in comprehending the intricate process of political socialization. Methods: An inherent strength of the rough set lies in its reliance solely on raw data, devoid of external inputs. The decision-theoretic rough set framework, an evolution of the rough set, has garnered widespread application across diverse domains, serving as a proficient tool for acquiring knowledge, particularly in navigating situations marked by vagueness and uncertainty. Despite the proliferation of mathematical models designed to discern people's voting behavior, a decision-based rough set recommendation remains noticeably absent in existing literature. This paper introduces an innovative three-way decision approach grounded in linguistic information for identifying voting behavior. The proposed approach is based on a hybrid probabilistic rough fuzzy model incorporating linguistic information and providing insights into voting patterns. Results: The three-way decision hybrid models are tested on people and a highly satisfactory result was achieved for identifying their voting behaviours. The justification of results was validated through the mathematical process. Conclusion: A practical illustration is provided to highlight the importance of this hybrid model and to confirm its usefulness in identifying and forecasting voting behaviour.

Electromagnetic (EM) wave has become an integral part of this electronics dominated era with extensive application in wireless communication, health care, and military applications. This has led to the development of EM wave absorbers for camouflaging the military warheads from the enemy radar tracking systems. The development of these absorbers requires considerable resources viz. time, energy, raw materials and cost. This creates a need for selection of a better absorber from the existing ones rather than the development of a new one to save the valuable resources. The judicious selection of an efficient microwave absorber requires ranking the existing absorbers based on selection criteria. For the first time, an attempt has been made to apply the concept of multi-criteria decisionmaking methods viz. VIKOR and TOPSIS in ranking the microwave absorbers. Core-shell materials provides enhanced EM wave attenuation owing to their hierarchical composite structure which provides a better approach to tune the dielectric and magnetic properties of the composite for obtaining good impedance matching resulting into enhanced absorption. In the present work, magnitude of minimum reflection co-efficient value, absorber thickness, deviation from the central frequency (i.e. 10 GHz) and -10 dB bandwidth have been selected as the criteria for judiciously selecting suitable core-shell based EM wave absorbers in 2-18 GHz. The selected criterion has been assigned distinct weights using Analytic Hierarchy Process (AHP). The AHP derived weights have been employed in TOPSIS and VIKOR analysis for the absorbers ranking and selection.

Electromagnetic (EM) wave has become an integral part of this electronics dominated era with extensive application in wireless communication, health care, and military applications. This has led to the development of EM wave absorbers for camouflaging the military warheads from the enemy radar tracking systems. The development of these absorbers requires considerable resources viz. time, energy, raw materials and cost. This creates a need for selection of a better absorber from the existing ones rather than the development of a new one to save the valuable resources. The judicious selection of an efficient microwave absorber requires ranking the existing absorbers based on selection criteria. For the first time, an attempt has been made to apply the concept of multi-criteria decisionmaking methods viz. VIKOR and TOPSIS in ranking the microwave absorbers. Core-shell materials provides enhanced EM wave attenuation owing to their hierarchical composite structure which provides a better approach to tune the dielectric and magnetic properties of the composite for obtaining good impedance matching resulting into enhanced absorption. In the present work, magnitude of minimum reflection co-efficient value, absorber thickness, deviation from the central frequency (i.e. 10 GHz) and -10 dB bandwidth have been selected as the criteria for judiciously selecting suitable core-shell based EM wave absorbers in 2-18 GHz. The selected criterion has been assigned distinct weights using Analytic Hierarchy Process (AHP). The AHP derived weights have been employed in TOPSIS and VIKOR analysis for the absorbers ranking and selection.

During the last three decades, integer‐valued autoregressive process of order p [or INAR(p)] based on different operators have been proposed as a natural, intuitive and maybe efficient model for integer‐valued time‐series data. However, this literature is surprisingly mute on the usefulness of the standard AR(p) process, which is otherwise meant for continuous‐valued time‐series data. In this paper, we attempt to explore the usefulness of the standard AR(p) model for obtaining coherent forecasting from integer‐valued time series. First, some advantages of this standard Box–Jenkins's type AR(p) process are discussed. We then carry out our some simulation experiments, which show the adequacy of the proposed method over the available alternatives. Our simulation results indicate that even when samples are generated from INAR(p) process, Box–Jenkins's model performs as good as the INAR(p) processes especially with respect to mean forecast. Two real data sets have been employed to study the expediency of the standard AR(p) model for integer‐valued time‐series data.

"This book provides proven methods for supplying uninterrupted, quality power at a reasonable price to the consumer. Taking into consideration operating constraints as well as generation cost, line overload, and congestion for expected and inadvertent loading stress, the text presents solutions based on stochastic optimization techniques--such as genetic algorithm, particle swarm optimization, and differential evolution--for improving the stability, reliability, and efficiency of LPS and deregulated multi-bus networks. It also proposes the use of stochastic techniques in optimizing utility management in the smart grid"--