In this work, an attempt is made to estimate time varying parameters in a linear stochastic differential equation. By defining mk as the local admissible sample/data observation size at time tk, parameters and state at time tk are estimated using past data on interval [tk−mk+1, tk]. We show that the parameter estimates at each time tk converge in probability to the true value of the parameters being estimated. A numerical simulation is presented by applying the local lagged adapted generalized method of moments (LLGMM) method to the stochastic differential models governing prices of energy commodities and stock price processes.
Otunuga,Osegun, M. (2017). Time varying parameter estimation scheme for a linear stochastic differential equation. International Journal of Statistics and Probability, 6(5), 84-100. https://doi.org/10.5539/ijsp.v6n5p84