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UD-based pairwise and MIMO Kalman-like filtering for estimation of econometric model structures
Optimization and Control · Submitted 18 Feb 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our methods are fast and accurate because their analytically computed gradient is utilized in the calculation instead of its numerical approximation.
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The abstract, sentence by sentence
One of the modern research lines in econometrics studies focuses on translating a wide variety of structural econometric models into their state-space form, which allows for efficient unknown dynamic system state and parameter estimations by the Kalman filtering scheme. The mentioned trend yields advanced state-space model structures, which demand innovative estimation techniques driven by application requirements to be devised. This paper explores both the linear time-invariant multiple-input, multiple-output system (LTI MIMO) and the pairwise Markov model (PMM) with the related pairwise Kalman filter (PKF). In particular, we design robust gradient-based adaptive Kalman-like filtering methods for the simultaneous state and parameter estimation in the outlined model structures. Our methods are fast and accurate because their analytically computed gradient is utilized in the calculation instead of its numerical approximation. Also, these employ the numerically robust $UDU^\top$-factorization-based Kalman filter implementation, which is reliable in practice. Our novel techniques are examined on numerical examples and used for treating one stochastic model in econometrics.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | One of the modern research lines in econometrics studies focuses on translating a wide variety of structural econometric models into their state-space form, which allows for efficient unknown dynamic system state and parameter estimations by the Kalman filtering scheme. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s1 | The mentioned trend yields advanced state-space model structures, which demand innovative estimation techniques driven by application requirements to be devised. | background 0.61 | 0.05 | 0.03 | asserted 0.98 |
| s2 | This paper explores both the linear time-invariant multiple-input, multiple-output system (LTI MIMO) and the pairwise Markov model (PMM) with the related pairwise Kalman filter (PKF). | contribution 0.74 | 0.65 | 0.05 | asserted 0.98 |
| s3 | In particular, we design robust gradient-based adaptive Kalman-like filtering methods for the simultaneous state and parameter estimation in the outlined model structures. | contribution 0.72 | 0.95 | 0.16 | asserted 1.00 |
| s4 | Our methods are fast and accurate because their analytically computed gradient is utilized in the calculation instead of its numerical approximation. | method 0.49 | 0.89 | 0.13 | asserted 1.00 |
| s5 | Also, these employ the numerically robust $UDU^\top$-factorization-based Kalman filter implementation, which is reliable in practice. | method 0.93 | 0.79 | 0.06 | asserted 1.00 |
| s6 | Our novel techniques are examined on numerical examples and used for treating one stochastic model in econometrics. | contribution 0.59 | 0.95 | 0.27 | asserted 1.00 |
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The paper
Original abstract
One of the modern research lines in econometrics studies focuses on translating a wide variety of structural econometric models into their state-space form, which allows for efficient unknown dynamic system state and parameter estimations by the Kalman filtering scheme. The mentioned trend yields advanced state-space model structures, which demand innovative estimation techniques driven by application requirements to be devised. This paper explores both the linear time-invariant multiple-input, multiple-output system (LTI MIMO) and the pairwise Markov model (PMM) with the related pairwise Kalman filter (PKF). In particular, we design robust gradient-based adaptive Kalman-like filtering methods for the simultaneous state and parameter estimation in the outlined model structures. Our methods are fast and accurate because their analytically computed gradient is utilized in the calculation instead of its numerical approximation. Also, these employ the numerically robust $UDU^\top$-factorization-based Kalman filter implementation, which is reliable in practice. Our novel techniques are examined on numerical examples and used for treating one stochastic model in econometrics.
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