TY - JOUR
T1 - Local lagged adapted generalized method of moments
T2 - An innovative estimation and forecasting approach and its applications
AU - Otunuga, Olusegun M.
AU - Ladde, Gangaram S.
AU - Ladde, Nathan G.
N1 - Funding Information:
This research is supported by the Mathematical Sciences Division, the U.S. Army Office, under Grant Numbers W911NF-12-1-0090 and W911NF-15-1-0182.
Publisher Copyright:
© 2019 Walter de Gruyter GmbH, Berlin/Boston.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - conceptual computational/theoretical parameter estimation scheme
KW - forecasting
KW - mean square optimal procedure
KW - method of moments
KW - nonparametric
KW - reaction/response time delay
KW - simulation
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U2 - 10.1515/jtse-2016-0024
DO - 10.1515/jtse-2016-0024
M3 - Article
AN - SCOPUS:85060695794
SN - 2194-6507
VL - 11
JO - Journal of Time Series Econometrics
JF - Journal of Time Series Econometrics
IS - 1
M1 - 20160024
ER -