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State space modeling of time series and dynamic econometric models / Abdelraheam Ahmed Mohammed ; Supervised Ghazal A. Amer

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Abdelraheam Ahmed Mohammed , 2014Description: 164 Leaves : charts ; 30cmOther title:
  • نمذجة فضاء الحاله للسلاسل الزمنية والاقتصاد القياسى الديناميكى [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Statistics and Econometrics Summary: In this thesis, we represent various multiple time series and dynamic econometric models in state space form, and estimate the vector of unknown parameters in the system matrices by Maximum Likelihood method for the prediction error decomposition of the log likelihood function using numerical computation methods to find the solution, then we treat the fitted model to start up the Kalman filter algorithm to estimate the first two moments in state and observational vectors in the prediction, smoothing and forecasting step. We consider two applications for the state space model, Local level model as a time invariant model for the Nile river data from 1871 to 2010 and CAPM as a fixed coefficients model for the monthly simple excess returns of CIB stock from January 2001 to December 2010, we use the simple excess returns of EGX30 as the market returns
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.04.M.Sc.2014.Ab.S (Browse shelf(Opens below)) Not for loan 01010110063841000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.04.M.Sc.2014.Ab.S (Browse shelf(Opens below)) 63841.CD Not for loan 01020110063841000

Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Statistics and Econometrics

In this thesis, we represent various multiple time series and dynamic econometric models in state space form, and estimate the vector of unknown parameters in the system matrices by Maximum Likelihood method for the prediction error decomposition of the log likelihood function using numerical computation methods to find the solution, then we treat the fitted model to start up the Kalman filter algorithm to estimate the first two moments in state and observational vectors in the prediction, smoothing and forecasting step. We consider two applications for the state space model, Local level model as a time invariant model for the Nile river data from 1871 to 2010 and CAPM as a fixed coefficients model for the monthly simple excess returns of CIB stock from January 2001 to December 2010, we use the simple excess returns of EGX30 as the market returns

Issued also as CD

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