Document Type
Article
Publication Date
1-2019
Abstract
In this work, an attempt is made to apply the Local Lagged Adapted Generalized Method of Moments (LLGMM) to estimate state and parameters in stochastic differential dynamic models. The development of LLGMM is motivated by parameter and state estimation problems in continuous-time nonlinear and non-stationary stochastic dynamic model validation problems in biological, chemical, engineering, energy commodity markets, financial, medical, military, physical sciences and social sciences. The byproducts of this innovative approach (LLGMM) are the balance between model specification and model prescription of continuous-time dynamic process and the development of discrete-time interconnected dynamic model of local sample mean and variance statistic process (DTIDMLSMVSP). Moreover, LLGMM is a dynamic non-parametric method. The DTIDMLSMVSP is an alternative approach to the GARCH (1,1) model, and it provides an iterative scheme for updating statistic coefficients in a system of generalized method of moment/observation equations. Furthermore, applications of LLGMM to energy commodities price, U.S. Treasury Bill interest rate and the U.S.–U.K. foreign exchange rate data strongly exhibit its unique role, scope and performance, in particular, in forecasting and confidence-interval problems in applied statistics.
Recommended Citation
Otunuga, O. M., Ladde, G. S., & Ladde, N. G. (2019). Local Lagged Adapted Generalized Method of Moments: An Innovative Estimation and Forecasting Approach and its Applications. Journal of Time Series Econometrics. 11(1): pp. https://doi.org/10.1515/jtse-2016-0024
Comments
This is an unpublished version of the authors’ manuscript. The final publication is available at www.degruyter.com.
Copyright © 2019 Journal of Time Series Econometrics. All rights reserved.