In this paper the identification problem is considered for initial conditionsin a non-minimal state-space model that includes interpretable state variablesgenerated by non-stationary stochastic processes. In order to solve theidentification problem, structural restrictions are imposed on initial conditionsin a state-space model with redundant state variables. The correspondingrestricted maximum likelihood estimator of initial conditions is derived.The restricted estimator of initial conditions can be used in order tocompute uniquely identified realizations of interpretable latent variables. Theidentification problem is illustrated analytically using a simple structuraleconomic model.

This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach.

In the field of power and drive systems, electrical AC machines are mostly modeled using a set of explicit ordinary differential equations in a state space representation. It is shown, that by using other equation types for simulation, algebraic constraints arising from aggregating several machines to a more complex system can directly be considered. The effects of different model variants on numerical ODE/DAE solvers are investigated in the focus of this work in order perform efficient simulations of larger systems possessing electrical AC machines.

We study the autocovariance structure of a general Markov switching second-order stationary VARMA model.Then we give stable finite order VARMA(p*, q*) representations for those M-state Markov switching VARMA(p, q) processes where the observables are uncorrelated with the regime variables. This allows us to obtain sharper bounds for p* and q* with respect to the ones existing in literature. Our results provide new insights into stochastic properties and facilitate statistical inference about the orders of MS-VARMA models and the underlying number of hidden states.

In this paper a semi-structural econometric model is implemented in order to estimate the natural rates of interest in two large economies of the Euro Area: Germany an Italy. The estimates suggest that after the financial crisis of 2007–2008 a decrease of the growth rate of potential output and the corresponding natural rate of interest was greater in Italy than in Germany which could have had important implications for the effectiveness of a common monetary policy. Unlike in other studies, it is found that the monetary policy stance was less expansionary in Italy as compared to Germany for the whole after-crisis period.

This paper proposes two nonlinear exact and simple state space models of a Zsource converter (ZSC) connected to an ac grid. A generic model of a ZSC accompanied with proper controllers are proposed and a dynamic model of the whole system is derived; as a result, based on a simple one, an equivalent block diagram of the current-controlled ZSC system is proposed. The ac small signal stability method is applied and the impact of controller parameters on network’s stability is discussed. Besides, overall system dynamic performance has been assessed in the event of perturbations. Time-domain simulations have been implemented in PSCAD/EMTDC to validate the accuracy of the models and effectiveness of the proposed controllers. The results of the exact model are compared with the response of the equations which are applied in MATLAB.

This research presents an advanced control approach for battery management in battery electric utility vehicles (BEUV) operating in indoor logistics environments. The proposed approach utilizes a combination of proportional-integral (PI), fuzzy PI, and interval type 2 fuzzy PI (IT2fuzzyPI) control structures to augment the state space model for battery management. The state space model incorporates the voltage and current of each battery cell as state variables and considers the current demand from the electric motor as an input. By integrating fuzzy logic with PI control and considering uncertainty, the IT2fuzzyPI structure offers improved control recital and system robustness in the occurrence of nonlinearities, uncertainties, and turbulences. The outcomes of the simulation validate the effectiveness of the proposed scheme in managing the battery pack system’s state of charge and controlling the rates of charging and discharging. The IT2fuzzyPI control significantly improves the overall proficiency and longevity of the battery system, making it suitable for battery electric utility vehicles in logistics environments. This research contributes to the field of battery management systems, providing a valuable tool for designing and evaluating high-performance electric vehicles with enhanced control capabilities.