US yield curve dynamics are subject to time-variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time-varying parameters and stochastic volatility, which treats the nature of parameter as unknown. Coefficients can evolve according random walk, Markov switching process, observed predictors, or depend on mixture these. To decide fo...

We consider a purely fractionally deferenced process driven by periodically time-varying long memory parameter. will build an estimate for the vector parameters using minimum Hellinger distance estimation. The results are investigated through simulation studies.

Abstract Estimating and quantifying uncertainty in unknown system parameters from limited data remains a challenging inverse problem variety of real-world applications. While many approaches focus on estimating constant parameters, subset these problems includes time-varying with evolution models that often cannot be directly observed. This work develops systematic particle filtering approach r...

The asymptotic behaviour is studied for a class of non-linear distributed parameter timevarying dissipative systems. This is achieved by using time-varying infinite-dimensional Banach state space description. Stability criteria are established, which are based on the dissipativity of the system in addition to another technical condition. The general development is applied to semi-linear systems...

In this paper, a novel Particle Swarm Optimization (PSO) identification algorithm for time-varying systems with a colored noise is presented. Presented criterion function can show not only outside system output error but also inside parameters error in order to explain more difference between actual and estimative system. Identification algorithm may consist of many different PSO algorithms tha...