A simulation exercise based on a simple intertemporal model shows that the estimated parameter values are very close to their population values, which provide further support for the estimation methodology introduced in this paper
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, we employ a non-linear state space model and the extended Kalman filter to simultaneously estimate the time-varying parameters in an optimal control problem, where the objective (loss) function is quadratic.
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Key Findings & Data:Our methodology also allows us to derive the difference between the optimal control and the observed control variable.
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Conclusion & Impact:A simulation exercise based on a simple intertemporal model shows that the estimated parameter values are very close to their population values, which provide further support for the estimation methodology introduced in this paper
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Non-linear state space models · Extended Kalman filter · Optimal linear regulator 1Ozlale (2003) and Salemi (1995) are some of the exceptions in this context
Abstract
In this paper, we employ a non-linear state space model and the extended Kalman filter to simultaneously estimate the time-varying parameters in an optimal control problem, where the objective (loss) function is quadratic. Our methodology also allows us to derive the difference between the optimal control and the observed control variable. A simulation exercise based on a simple intertemporal model shows that the estimated parameter values are very close to their population values, which provide further support for the estimation methodology introduced in this paper
ÖZBEK, L., BABACAN, E. K. (2015). ESTIMATION OF TIME VARYING PARAMETERS IN AN OPTIMAL CONTROL PROBLEM. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, Cilt: 64 - Sayı: 2, 111–121. https://ajindex.com/articles/747348
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