IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/22734.html
   My bibliography  Save this paper

Kalman Filter and its Economic Applications

Author

Listed:
  • Pasricha, Gurnain Kaur

Abstract

This paper is an eclectic study of the uses of the Kalman filter in existing econometric literature. An effort is made to introduce the various extensions to the linear filter first developed by Kalman(1960) through examples of their uses in economics. The basic filter is first derived and then some applications are reviewed.

Suggested Citation

  • Pasricha, Gurnain Kaur, 2006. "Kalman Filter and its Economic Applications," MPRA Paper 22734, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:22734
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/22734/1/MPRA_paper_22734.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ozbek, Levent & Ozlale, Umit, 2005. "Employing the extended Kalman filter in measuring the output gap," Journal of Economic Dynamics and Control, Elsevier, vol. 29(9), pages 1611-1622, September.
    2. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(2), pages 247-264.
    3. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rocio Elizondo, 2019. "Estimaciones del PIB mensual en México basadas en el IGAE/Monthly GDP estimates in Mexico based on the IGAE," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 34(2), pages 197-241.
    2. Hui ‘Fox’ Ling & Douglas B. Stone, 2016. "Time-varying forecasts by variational approximation of sequential Bayesian inference," Quantitative Finance, Taylor & Francis Journals, vol. 16(1), pages 43-67, January.
    3. Jilong Chen & Christian Ewald & Ruolan Ouyang & Sjur Westgaard & Xiaoxia Xiao, 2022. "Pricing commodity futures and determining risk premia in a three factor model with stochastic volatility: the case of Brent crude oil," Annals of Operations Research, Springer, vol. 313(1), pages 29-46, June.
    4. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    5. Sergey Sinelnikov-Murylev & Sergey Drobyshevsky & Maria Kazakova & Michael Alexeev, 2016. "Decomposition of Russia's GDP Growth Rates," Research Paper Series, Gaidar Institute for Economic Policy, issue 167P, pages 123-123.
    6. Yüksel, Ebru & Metin-Ozcan, Kivilcim & Hatipoglu, Ozan, 2013. "A survey on time-varying parameter Taylor rule: A model modified with interest rate pass-through," Economic Systems, Elsevier, vol. 37(1), pages 122-134.
    7. Carlos David Ardila-Dueñas & Hernán Rincón-Castro, 2019. "¿Cómo y qué tanto impacta la deuda pública a las tasas de interés de mercado?," Borradores de Economia 1077, Banco de la Republica de Colombia.
    8. Aguilar-Argaez Ana María & Elizondo Rocío & Roldán-Peña Jessica, 2016. "Break-Even-Inflation's Decomposition in Mexico," Working Papers 2016-22, Banco de México.
    9. Kawashima, Shigekazu & Sari, Deffi Ayu Puspito, 2010. "Time-varying Armington elasticity and country-of-origin bias: from the dynamic perspective of the Japanese demand for beef imports," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 54(1), pages 1-15.
    10. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.
    11. Christian-Oliver Ewald & Aihua Zhang & Zhe Zong, 2019. "On the calibration of the Schwartz two-factor model to WTI crude oil options and the extended Kalman Filter," Annals of Operations Research, Springer, vol. 282(1), pages 119-130, November.
    12. Elizondo Rocío, 2012. "Monthly GDP estimates based on the IGAE," Working Papers 2012-11, Banco de México.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Monica Billio & Maddalena Cavicchioli, 2013. "�Markov Switching Models for Volatility: Filtering, Approximation and Duality�," Working Papers 2013:24, Department of Economics, University of Venice "Ca' Foscari".
    2. Gonul Sengul & Murat Tasci, 2014. "Unemployment Flows, Participation and the Natural Rate for Turkey," Working Papers 1435, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    3. Charles Bos & Neil Shephard, 2006. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 219-244.
    4. Chung-Ming Kuan, 2013. "Markov switching model (in Russian)," Quantile, Quantile, issue 11, pages 13-40, December.
    5. Ramaprasad Bhar, 2010. "Stochastic Filtering with Applications in Finance," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 7736, January.
    6. Charles Bos & Neil Shephard, 2006. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 219-244.

    More about this item

    Keywords

    Kalman Filter; Time-varying Parameters; Stochastic Volatility; Markov Switching;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:22734. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.