IDEAS home Printed from https://ideas.repec.org/a/eee/dyncon/v125y2021ics0165188921000324.html
   My bibliography  Save this article

Latent variables analysis in structural models: A New decomposition of the kalman smoother

Author

Listed:
  • Chung, Hess
  • Fuentes-Albero, Cristina
  • Paustian, Matthias
  • Pfajfar, Damjan

Abstract

Standard latent variable analysis in structural state space models decomposes latent variables into contributions of structural shocks (shock decomposition), or into contributions of the observable variables (data decomposition). We propose to link the shock decomposition of the latent variables and the data decomposition of the structural shocks in what we call the double decomposition. This decomposition allows us to better gauge the influence of data on latent variables by taking into account the transmission mechanism of each type of shock. We show the usefulness of the double decomposition by analyzing the role of observable variables in estimating the output gap in two models and by studying the role of news in revisions of the output gap.

Suggested Citation

  • Chung, Hess & Fuentes-Albero, Cristina & Paustian, Matthias & Pfajfar, Damjan, 2021. "Latent variables analysis in structural models: A New decomposition of the kalman smoother," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).
    • Handle: RePEc:eee:dyncon:v:125:y:2021:i:c:s0165188921000324
    DOI: 10.1016/j.jedc.2021.104097
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165188921000324
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jedc.2021.104097?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Marco Del Negro & Marc P. Giannoni & Frank Schorfheide, 2015. "Inflation in the Great Recession and New Keynesian Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 168-196, January.
    2. Nicholas Sander, 2013. "Fresh perspectives on unobservable variables: Data decomposition of the Kalman smoother," Reserve Bank of New Zealand Analytical Notes series AN2013/09, Reserve Bank of New Zealand.
    3. Peter N. Ireland, 2011. "A New Keynesian Perspective on the Great Recession," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 31-54, February.
    4. Andrle, Michal, 2012. "Understanding DSGE Filters in Forecasting and Policy Analysis," Dynare Working Papers 16, CEPREMAP.
    5. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    6. Robert Barsky & Alejandro Justiniano & Leonardo Melosi, 2014. "The Natural Rate of Interest and Its Usefulness for Monetary Policy," American Economic Review, American Economic Association, vol. 104(5), pages 37-43, May.
    Full references (including those not matched with items on IDEAS)

    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. Maik H. Wolters, 2018. "How the baby boomers' retirement wave distorts model‐based output gap estimates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 680-689, August.
    2. Enrico Sergio Levrero, 2021. "Estimates of the Natural Rate of Interest and the Stance of Monetary Policies: A Critical Assessment," International Journal of Political Economy, Taylor & Francis Journals, vol. 50(1), pages 5-27, February.
    3. Joshua Brault & Hashmat Khan & Louis Phaneuf & Jean Gardy Victor, 2021. "Did the Fed Remain at the ZLB Long Enough? Lessons from the 2008-2019 Period," Working Papers 21-09, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    4. Joshua Brault & Hashmat Khan & Louis Phaneuf & Jean-Gardy Victor, 2020. "Is Unconventional Monetary Policy Stabilizing? Evidence From the Great Recession and Recovery Years," Carleton Economic Papers 20-11, Carleton University, Department of Economics.
    5. Jannsen, Nils & Wolters, Maik H., 2016. "Zu Produktionspotenzial und Produktionslücke in den Vereinigten Staaten," Kiel Insight 2016.2, Kiel Institute for the World Economy (IfW Kiel).
    6. Gern, Klaus-Jürgen & Hauber, Philipp & Jannsen, Nils & Kooths, Stefan & Plödt, Martin & Wolters, Maik H., 2016. "Weltkonjunktur im Frühjahr 2016 - Getrübte Aussichten für die Weltkonjunktur [World Economy Spring 2016 - Clouded outlook for the world economy]," Kieler Konjunkturberichte 15, Kiel Institute for the World Economy (IfW Kiel).
    7. Rui Wang, 2019. "Unconventional Monetary Policy in Japan: Empirical Evidence from Estimated Shadow Rate DSGE Model," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-29, June.
    8. Marco Del Negro & Domenico Giannone & Marc P. Giannoni & Andrea Tambalotti, 2017. "Safety, Liquidity, and the Natural Rate of Interest," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 48(1 (Spring), pages 235-316.
    9. Belongia, Michael T. & Ireland, Peter N., 2022. "A reconsideration of money growth rules," Journal of Economic Dynamics and Control, Elsevier, vol. 135(C).
    10. Piotr Ciżkowicz & Andrzej Rzońca & Andrzej Torój, 2019. "In Search of an Appropriate Lower Bound. The Zero Lower Bound vs. the Positive Lower Bound under Discretion and Commitment," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 1028-1053, November.
    11. Campbell Leith & Eric Leeper, 2016. "Understanding Inflation as a Joint Monetary-Fiscal Phenomenon," Working Papers 2016_01, Business School - Economics, University of Glasgow.
    12. Neri, Stefano & Gerali, Andrea, 2019. "Natural rates across the Atlantic," Journal of Macroeconomics, Elsevier, vol. 62(C).
    13. Le, Vo Phuong Mai & Matthews, Kent & Meenagh, David & Minford, Patrick & Xiao, Zhiguo, 2021. "Shadow banks, banking policies and China’s macroeconomic fluctuations," Journal of International Money and Finance, Elsevier, vol. 116(C).
    14. Christopher M. Gunn, 2018. "Overaccumulation, Interest, and Prices," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(2-3), pages 479-511, March.
    15. Benjamín García & Sebastián Guarda & Markus Kirchner & Rodrigo Tranamil, 2019. "XMAS: An extended model for analysis and simulations," Working Papers Central Bank of Chile 833, Central Bank of Chile.
    16. Anna Grodecka-Messi, 2019. "Subprime borrowers, securitization and the transmission of business cycles," Canadian Journal of Economics, Canadian Economics Association, vol. 52(4), pages 1600-1654, November.
    17. Francesco Bianchi & Leonardo Melosi, 2017. "Escaping the Great Recession," American Economic Review, American Economic Association, vol. 107(4), pages 1030-1058, April.
    18. Susanto Basu & Brent Bundick, 2017. "Uncertainty Shocks in a Model of Effective Demand," Econometrica, Econometric Society, vol. 85, pages 937-958, May.
    19. Engin Kara & Ahmed Pirzada, 2020. "A Possible Explanation Of The Missing Deflation Puzzle," Economic Inquiry, Western Economic Association International, vol. 58(1), pages 361-373, January.
    20. Paul Ho & Pierre-Daniel G. Sarte & Felipe Schwartzman, 2022. "Multilateral Comovement in a New Keynesian World: A Little Trade Goes a Long Way," Working Paper 22-10, Federal Reserve Bank of Richmond.

    More about this item

    Keywords

    Kalman smoother; Latent variables; Shock decomposition; Data decomposition; Double decomposition;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:dyncon:v:125:y:2021:i:c:s0165188921000324. 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/jedc .

    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.