Analytical uses of Kalman filtering in econometrics — A survey
Abstract This paper surveys the different uses of Kalman filtering in the estimation of statistical (econometric) models. The Kalman filter will be portrayed as (i) a natural generalization of exponential smoothing with a time-dependent smoothing factor, (ii) a recursive estimation technique for a v...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Schneider, Wolfgang [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1988 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 1988 |
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| Übergeordnetes Werk: |
Enthalten in: Statistical papers - Springer-Verlag, 1988, 29(1988), 1 vom: Dez., Seite 3-33 |
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| Übergeordnetes Werk: |
volume:29 ; year:1988 ; number:1 ; month:12 ; pages:3-33 |
| Links: |
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| DOI / URN: |
10.1007/BF02924508 |
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| 520 | |a Abstract This paper surveys the different uses of Kalman filtering in the estimation of statistical (econometric) models. The Kalman filter will be portrayed as (i) a natural generalization of exponential smoothing with a time-dependent smoothing factor, (ii) a recursive estimation technique for a variety of econometric models amenable to a state space formulation in particular for econometric models with time varying coefficients (iii) an instrument for the recursive calculation of the likelihood of the (constant) state space coefficients (iv) a means of helping to implement the $ scoring^{−} $ and EM-method for iteratively maximizing this likelihood (v) an analytical tool in asymptotic estimation theory. The concluding section points to the importance of Kalman filtering for alternatives to $ maximum^{−} $ likelihood estimation of state space parameters. | ||
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10.1007/BF02924508 doi (DE-627)OLC2025013477 (DE-He213)BF02924508-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Schneider, Wolfgang verfasserin aut Analytical uses of Kalman filtering in econometrics — A survey 1988 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1988 Abstract This paper surveys the different uses of Kalman filtering in the estimation of statistical (econometric) models. The Kalman filter will be portrayed as (i) a natural generalization of exponential smoothing with a time-dependent smoothing factor, (ii) a recursive estimation technique for a variety of econometric models amenable to a state space formulation in particular for econometric models with time varying coefficients (iii) an instrument for the recursive calculation of the likelihood of the (constant) state space coefficients (iv) a means of helping to implement the $ scoring^{−} $ and EM-method for iteratively maximizing this likelihood (v) an analytical tool in asymptotic estimation theory. The concluding section points to the importance of Kalman filtering for alternatives to $ maximum^{−} $ likelihood estimation of state space parameters. State Space Kalman Filter State Space Model Exponential Smoothing Alman Enthalten in Statistical papers Springer-Verlag, 1988 29(1988), 1 vom: Dez., Seite 3-33 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:29 year:1988 number:1 month:12 pages:3-33 https://doi.org/10.1007/BF02924508 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_26 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_70 GBV_ILN_120 GBV_ILN_754 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4309 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4319 GBV_ILN_4324 GBV_ILN_4700 AR 29 1988 1 12 3-33 |
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10.1007/BF02924508 doi (DE-627)OLC2025013477 (DE-He213)BF02924508-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Schneider, Wolfgang verfasserin aut Analytical uses of Kalman filtering in econometrics — A survey 1988 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1988 Abstract This paper surveys the different uses of Kalman filtering in the estimation of statistical (econometric) models. The Kalman filter will be portrayed as (i) a natural generalization of exponential smoothing with a time-dependent smoothing factor, (ii) a recursive estimation technique for a variety of econometric models amenable to a state space formulation in particular for econometric models with time varying coefficients (iii) an instrument for the recursive calculation of the likelihood of the (constant) state space coefficients (iv) a means of helping to implement the $ scoring^{−} $ and EM-method for iteratively maximizing this likelihood (v) an analytical tool in asymptotic estimation theory. The concluding section points to the importance of Kalman filtering for alternatives to $ maximum^{−} $ likelihood estimation of state space parameters. State Space Kalman Filter State Space Model Exponential Smoothing Alman Enthalten in Statistical papers Springer-Verlag, 1988 29(1988), 1 vom: Dez., Seite 3-33 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:29 year:1988 number:1 month:12 pages:3-33 https://doi.org/10.1007/BF02924508 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_26 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_70 GBV_ILN_120 GBV_ILN_754 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4309 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4319 GBV_ILN_4324 GBV_ILN_4700 AR 29 1988 1 12 3-33 |
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10.1007/BF02924508 doi (DE-627)OLC2025013477 (DE-He213)BF02924508-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Schneider, Wolfgang verfasserin aut Analytical uses of Kalman filtering in econometrics — A survey 1988 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1988 Abstract This paper surveys the different uses of Kalman filtering in the estimation of statistical (econometric) models. The Kalman filter will be portrayed as (i) a natural generalization of exponential smoothing with a time-dependent smoothing factor, (ii) a recursive estimation technique for a variety of econometric models amenable to a state space formulation in particular for econometric models with time varying coefficients (iii) an instrument for the recursive calculation of the likelihood of the (constant) state space coefficients (iv) a means of helping to implement the $ scoring^{−} $ and EM-method for iteratively maximizing this likelihood (v) an analytical tool in asymptotic estimation theory. The concluding section points to the importance of Kalman filtering for alternatives to $ maximum^{−} $ likelihood estimation of state space parameters. State Space Kalman Filter State Space Model Exponential Smoothing Alman Enthalten in Statistical papers Springer-Verlag, 1988 29(1988), 1 vom: Dez., Seite 3-33 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:29 year:1988 number:1 month:12 pages:3-33 https://doi.org/10.1007/BF02924508 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_26 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_70 GBV_ILN_120 GBV_ILN_754 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4309 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4319 GBV_ILN_4324 GBV_ILN_4700 AR 29 1988 1 12 3-33 |
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10.1007/BF02924508 doi (DE-627)OLC2025013477 (DE-He213)BF02924508-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Schneider, Wolfgang verfasserin aut Analytical uses of Kalman filtering in econometrics — A survey 1988 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1988 Abstract This paper surveys the different uses of Kalman filtering in the estimation of statistical (econometric) models. The Kalman filter will be portrayed as (i) a natural generalization of exponential smoothing with a time-dependent smoothing factor, (ii) a recursive estimation technique for a variety of econometric models amenable to a state space formulation in particular for econometric models with time varying coefficients (iii) an instrument for the recursive calculation of the likelihood of the (constant) state space coefficients (iv) a means of helping to implement the $ scoring^{−} $ and EM-method for iteratively maximizing this likelihood (v) an analytical tool in asymptotic estimation theory. The concluding section points to the importance of Kalman filtering for alternatives to $ maximum^{−} $ likelihood estimation of state space parameters. State Space Kalman Filter State Space Model Exponential Smoothing Alman Enthalten in Statistical papers Springer-Verlag, 1988 29(1988), 1 vom: Dez., Seite 3-33 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:29 year:1988 number:1 month:12 pages:3-33 https://doi.org/10.1007/BF02924508 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_26 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_70 GBV_ILN_120 GBV_ILN_754 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4309 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4319 GBV_ILN_4324 GBV_ILN_4700 AR 29 1988 1 12 3-33 |
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10.1007/BF02924508 doi (DE-627)OLC2025013477 (DE-He213)BF02924508-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Schneider, Wolfgang verfasserin aut Analytical uses of Kalman filtering in econometrics — A survey 1988 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1988 Abstract This paper surveys the different uses of Kalman filtering in the estimation of statistical (econometric) models. The Kalman filter will be portrayed as (i) a natural generalization of exponential smoothing with a time-dependent smoothing factor, (ii) a recursive estimation technique for a variety of econometric models amenable to a state space formulation in particular for econometric models with time varying coefficients (iii) an instrument for the recursive calculation of the likelihood of the (constant) state space coefficients (iv) a means of helping to implement the $ scoring^{−} $ and EM-method for iteratively maximizing this likelihood (v) an analytical tool in asymptotic estimation theory. The concluding section points to the importance of Kalman filtering for alternatives to $ maximum^{−} $ likelihood estimation of state space parameters. State Space Kalman Filter State Space Model Exponential Smoothing Alman Enthalten in Statistical papers Springer-Verlag, 1988 29(1988), 1 vom: Dez., Seite 3-33 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:29 year:1988 number:1 month:12 pages:3-33 https://doi.org/10.1007/BF02924508 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_26 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_70 GBV_ILN_120 GBV_ILN_754 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4309 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4319 GBV_ILN_4324 GBV_ILN_4700 AR 29 1988 1 12 3-33 |
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Abstract This paper surveys the different uses of Kalman filtering in the estimation of statistical (econometric) models. The Kalman filter will be portrayed as (i) a natural generalization of exponential smoothing with a time-dependent smoothing factor, (ii) a recursive estimation technique for a variety of econometric models amenable to a state space formulation in particular for econometric models with time varying coefficients (iii) an instrument for the recursive calculation of the likelihood of the (constant) state space coefficients (iv) a means of helping to implement the $ scoring^{−} $ and EM-method for iteratively maximizing this likelihood (v) an analytical tool in asymptotic estimation theory. The concluding section points to the importance of Kalman filtering for alternatives to $ maximum^{−} $ likelihood estimation of state space parameters. © Springer-Verlag 1988 |
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Abstract This paper surveys the different uses of Kalman filtering in the estimation of statistical (econometric) models. The Kalman filter will be portrayed as (i) a natural generalization of exponential smoothing with a time-dependent smoothing factor, (ii) a recursive estimation technique for a variety of econometric models amenable to a state space formulation in particular for econometric models with time varying coefficients (iii) an instrument for the recursive calculation of the likelihood of the (constant) state space coefficients (iv) a means of helping to implement the $ scoring^{−} $ and EM-method for iteratively maximizing this likelihood (v) an analytical tool in asymptotic estimation theory. The concluding section points to the importance of Kalman filtering for alternatives to $ maximum^{−} $ likelihood estimation of state space parameters. © Springer-Verlag 1988 |
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Abstract This paper surveys the different uses of Kalman filtering in the estimation of statistical (econometric) models. The Kalman filter will be portrayed as (i) a natural generalization of exponential smoothing with a time-dependent smoothing factor, (ii) a recursive estimation technique for a variety of econometric models amenable to a state space formulation in particular for econometric models with time varying coefficients (iii) an instrument for the recursive calculation of the likelihood of the (constant) state space coefficients (iv) a means of helping to implement the $ scoring^{−} $ and EM-method for iteratively maximizing this likelihood (v) an analytical tool in asymptotic estimation theory. The concluding section points to the importance of Kalman filtering for alternatives to $ maximum^{−} $ likelihood estimation of state space parameters. © Springer-Verlag 1988 |
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