IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v168y2018icp42-45.html
   My bibliography  Save this article

A note on low-dimensional Kalman smoothers for systems with lagged states in the measurement equation

Author

Listed:
  • Kurz, Malte S.

Abstract

In this paper we derive a modified Kalman smoother for state space systems with lagged states in the measurement equation. This modified Kalman smoother minimizes the mean squared error (MSE). Computationally efficient algorithms that can be used to implement it in practice are discussed. We also show that the conjecture in Nimark (2015) that the output of his modified Kalman filter for this type of systems can be plugged into the standard Kalman smoother is in general not correct. The competing smoothers are compared with regards to the MSE.

Suggested Citation

  • Kurz, Malte S., 2018. "A note on low-dimensional Kalman smoothers for systems with lagged states in the measurement equation," Economics Letters, Elsevier, vol. 168(C), pages 42-45.
    • Handle: RePEc:eee:ecolet:v:168:y:2018:i:c:p:42-45
    DOI: 10.1016/j.econlet.2018.03.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176518301289
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2018.03.037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    2. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    3. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    4. Nimark, Kristoffer P., 2015. "A low dimensional Kalman filter for systems with lagged states in the measurement equation," Economics Letters, Elsevier, vol. 127(C), pages 10-13.
    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. Pagan, Adrian & Robinson, Tim, 2022. "Excess shocks can limit the economic interpretation," European Economic Review, Elsevier, vol. 145(C).
    2. Adrian Pagan & Tim Robinson, 2020. "Too Many Shocks Spoil the Interpretation," Melbourne Institute Working Paper Series wp2020n02, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    3. Hauber, Philipp & Schumacher, Christian & Zhang, Jiachun, 2019. "A flexible state-space model with lagged states and lagged dependent variables: Simulation smoothing," Discussion Papers 15/2019, Deutsche Bundesbank.

    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. Hauber, Philipp & Schumacher, Christian & Zhang, Jiachun, 2019. "A flexible state-space model with lagged states and lagged dependent variables: Simulation smoothing," Discussion Papers 15/2019, Deutsche Bundesbank.
    2. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model," Tinbergen Institute Discussion Papers 08-069/4, Tinbergen Institute.
    3. Koopman, Siem Jan & Lucas, André, 2008. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 510-525.
    4. McCausland, William J. & Miller, Shirley & Pelletier, Denis, 2011. "Simulation smoothing for state-space models: A computational efficiency analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 199-212, January.
    5. David de Antonio Liedo, 2014. "Nowcasting Belgium," Working Paper Research 256, National Bank of Belgium.
    6. Rob Luginbuhl, 2020. "Estimation of the Financial Cycle with a Rank-Reduced Multivariate State-Space Model," CPB Discussion Paper 409, CPB Netherlands Bureau for Economic Policy Analysis.
    7. Martín Almuzara & Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2024. "GDP Solera: The Ideal Vintage Mix," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 984-997, July.
    8. Siem Jan Koopman & André Lucas & Marcel Scharth, 2016. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 97-110, March.
    9. Falk Bräuning & Siem Jan Koopman, 2016. "The dynamic factor network model with an application to global credit risk," Working Papers 16-13, Federal Reserve Bank of Boston.
    10. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
    11. Tommaso Proietti, 2012. "Seasonality, Forecast Extensions And Business Cycle Uncertainty," Journal of Economic Surveys, Wiley Blackwell, vol. 26(4), pages 555-569, September.
    12. Mesters, G. & Koopman, S.J., 2014. "Generalized dynamic panel data models with random effects for cross-section and time," Journal of Econometrics, Elsevier, vol. 180(2), pages 127-140.
    13. Iacopini, Matteo & Poon, Aubrey & Rossini, Luca & Zhu, Dan, 2023. "Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP," Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
    14. repec:wrk:wrkemf:11 is not listed on IDEAS
    15. Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
    16. Thomas Hasenzagl & Filippo Pellegrino & Lucrezia Reichlin & Giovanni Ricco, 2022. "A Model of the Fed's View on Inflation," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 686-704, October.
    17. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    18. Brand, Claus & Goy, Gavin W & Lemke, Wolfgang, 2020. "Natural rate chimera and bond pricing reality," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224546, Verein für Socialpolitik / German Economic Association.
    19. Jouchi Nakajima, 2011. "Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 29, pages 107-142, November.
    20. Michel Beine & Charles S. Bos & Sébastien Laurent, 2007. "The Impact of Central Bank FX Interventions on Currency Components," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 154-183.
    21. Alastair Cunningham & Jana Eklund & Chris Jeffery & George Kapetanios & Vincent Labhard, 2009. "A State Space Approach to Extracting the Signal From Uncertain Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 173-180, March.

    More about this item

    Keywords

    Kalman filter; Kalman smoother; Lagged states;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

    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:eee:ecolet:v:168:y:2018:i:c:p:42-45. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.