Stochastic optimal control of economic growth model under research and development investment with Kalman filtering approaches
This article examines an economic growth model that expresses the interaction between production, technology stock, and research and development (R&D) investments. The goal of this study is to maximize production. Considering the presence of Gaussian white noises, this model is reformulated as a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Hunan University
2022
|
| _version_ | 1796984281444122624 |
|---|---|
| author | Lim, Yue Yuin Kek, Sie Long Leong, Wah June |
| author_facet | Lim, Yue Yuin Kek, Sie Long Leong, Wah June |
| author_sort | Lim, Yue Yuin |
| collection | UPM |
| description | This article examines an economic growth model that expresses the interaction between production, technology stock, and research and development (R&D) investments. The goal of this study is to maximize production. Considering the presence of Gaussian white noises, this model is reformulated as a stochastic optimal control problem, where the R&D investment rate is defined as the control input. We aim to explore the efficiency of Kalman filtering approaches for solving this problem. Here, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) are applied for state estimation. The state equation linearization is made in the EKF, while the unscented transform is taken in the UKF for generating a set of sigma points. These approaches aim to estimate the state dynamics from different perspectives. With these state estimates, two different computational algorithms are proposed, the EKF for state-control (EKF4SC) and UKF for state-control (UKF4SC) algorithms. The optimal control policy is designed to minimize the cost function. For illustration, the model's parameters are considered in the simulation experiment. The simulation results showed that the UKF has higher accuracy in the state estimation with the smallest mean squares of error compared with the EKF. Moreover, the optimal control policy based on the state estimate generated from the UKF could optimize the cost function of the problem. Hence, the results show the efficiency of the algorithms proposed. In conclusion, the application of the Kalman filtering algorithms to the economic growth model is presented. The significance of this study is to provide a stochastic optimal control model for the economic growth problem and to suggest an efficient computational approach for solving this stochastic optimal control problem. |
| first_indexed | 2024-03-06T11:18:08Z |
| format | Article |
| id | upm.eprints-103300 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:18:08Z |
| publishDate | 2022 |
| publisher | Hunan University |
| record_format | dspace |
| spelling | upm.eprints-1033002023-06-26T07:24:36Z http://psasir.upm.edu.my/id/eprint/103300/ Stochastic optimal control of economic growth model under research and development investment with Kalman filtering approaches Lim, Yue Yuin Kek, Sie Long Leong, Wah June This article examines an economic growth model that expresses the interaction between production, technology stock, and research and development (R&D) investments. The goal of this study is to maximize production. Considering the presence of Gaussian white noises, this model is reformulated as a stochastic optimal control problem, where the R&D investment rate is defined as the control input. We aim to explore the efficiency of Kalman filtering approaches for solving this problem. Here, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) are applied for state estimation. The state equation linearization is made in the EKF, while the unscented transform is taken in the UKF for generating a set of sigma points. These approaches aim to estimate the state dynamics from different perspectives. With these state estimates, two different computational algorithms are proposed, the EKF for state-control (EKF4SC) and UKF for state-control (UKF4SC) algorithms. The optimal control policy is designed to minimize the cost function. For illustration, the model's parameters are considered in the simulation experiment. The simulation results showed that the UKF has higher accuracy in the state estimation with the smallest mean squares of error compared with the EKF. Moreover, the optimal control policy based on the state estimate generated from the UKF could optimize the cost function of the problem. Hence, the results show the efficiency of the algorithms proposed. In conclusion, the application of the Kalman filtering algorithms to the economic growth model is presented. The significance of this study is to provide a stochastic optimal control model for the economic growth problem and to suggest an efficient computational approach for solving this stochastic optimal control problem. Hunan University 2022 Article PeerReviewed Lim, Yue Yuin and Kek, Sie Long and Leong, Wah June (2022) Stochastic optimal control of economic growth model under research and development investment with Kalman filtering approaches. Journal of Hunan University (Natural Sciences), 49 (6). 120 - 128. ISSN 1674-2974 http://jonuns.com/index.php/journal/article/view/1094 10.55463/issn.1674-2974.49.6.13 |
| spellingShingle | Lim, Yue Yuin Kek, Sie Long Leong, Wah June Stochastic optimal control of economic growth model under research and development investment with Kalman filtering approaches |
| title | Stochastic optimal control of economic growth model under research and development investment with Kalman filtering approaches |
| title_full | Stochastic optimal control of economic growth model under research and development investment with Kalman filtering approaches |
| title_fullStr | Stochastic optimal control of economic growth model under research and development investment with Kalman filtering approaches |
| title_full_unstemmed | Stochastic optimal control of economic growth model under research and development investment with Kalman filtering approaches |
| title_short | Stochastic optimal control of economic growth model under research and development investment with Kalman filtering approaches |
| title_sort | stochastic optimal control of economic growth model under research and development investment with kalman filtering approaches |
| work_keys_str_mv | AT limyueyuin stochasticoptimalcontrolofeconomicgrowthmodelunderresearchanddevelopmentinvestmentwithkalmanfilteringapproaches AT keksielong stochasticoptimalcontrolofeconomicgrowthmodelunderresearchanddevelopmentinvestmentwithkalmanfilteringapproaches AT leongwahjune stochasticoptimalcontrolofeconomicgrowthmodelunderresearchanddevelopmentinvestmentwithkalmanfilteringapproaches |