IDEAS home Printed from https://ideas.repec.org/p/ptu/wpaper/w202105.html
   My bibliography  Save this paper

Spectral decomposition of the information about latent variables in dynamic macroeconomic models

Author

Listed:
  • Nikolay Iskrev

Abstract

In this paper, I show how to perform spectral decomposition of the information about latent variables in dynamic economic models. A model describes the joint probability distribution of a set of observed and latent variables. The amount of information transferred from the former to the latter is measured by the reduction of uncertainty in the posterior compared to the prior distribution of any given latent variable. Casting the analysis in the frequency domain allows decomposing the total amount of information in terms of frequency-specific contributions as well as in terms of information contributed by individual observed variables. I illustrate the usefulness of the proposed methodology with applications to two DSGE models taken from the literature.

Suggested Citation

  • Nikolay Iskrev, 2021. "Spectral decomposition of the information about latent variables in dynamic macroeconomic models," Working Papers w202105, Banco de Portugal, Economics and Research Department.
    • Handle: RePEc:ptu:wpaper:w202105
    as

    Download full text from publisher

    File URL: https://www.bportugal.pt/sites/default/files/anexos/papers/wp202105.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alejandro Justiniano & Giorgio Primiceri & Andrea Tambalotti, 2011. "Investment Shocks and the Relative Price of Investment," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 101-121, January.
    2. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2011. "Non‐Fundamentalness in Structural Econometric Models: A Review," International Statistical Review, International Statistical Institute, vol. 79(1), pages 16-47, April.
    3. Hansen, Lars Peter & Sargent, Thomas J., 1980. "Formulating and estimating dynamic linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 7-46, May.
    4. Clive Granger & Jin‐Lung Lin, 1994. "Using The Mutual Information Coefficient To Identify Lags In Nonlinear Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(4), pages 371-384, July.
    5. Domenico Giannone & Lucrezia Reichlin, 2006. "Does information help recovering structural shocks from past observations?," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 455-465, 04-05.
    6. Stock, James H. & Watson, Mark W., 1999. "Business cycle fluctuations in us macroeconomic time series," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 1, pages 3-64, Elsevier.
    7. Lippi, Marco & Reichlin, Lucrezia, 1993. "The Dynamic Effects of Aggregate Demand and Supply Disturbances: Comment," American Economic Review, American Economic Association, vol. 83(3), pages 644-652, June.
    8. Massimo Franchi & Paolo Paruolo, 2015. "Minimality of State Space Solutions of DSGE Models and Existence Conditions for Their VAR Representation," Computational Economics, Springer;Society for Computational Economics, vol. 46(4), pages 613-626, December.
    9. Paul Beaudry & Patrick Fève & Alain Guay & Franck Portier, 2015. "When is Nonfundamentalness in VARs a Real Problem? An Application to News Shocks," NBER Working Papers 21466, National Bureau of Economic Research, Inc.
    10. Soccorsi, Stefano, 2016. "Measuring nonfundamentalness for structural VARs," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 86-101.
    11. Ryan Chahrour & Kyle Jurado, 2017. "Recoverability," Boston College Working Papers in Economics 935, Boston College Department of Economics.
    12. Franchi, Massimo & Vidotto, Anna, 2013. "A check for finite order VAR representations of DSGE models," Economics Letters, Elsevier, vol. 120(1), pages 100-103.
    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. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    2. Nikolay Iskrev, 2018. "Are asset price data informative about news shocks? A DSGE perspective," Working Papers REM 2018/33, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    3. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    4. Soccorsi, Stefano, 2016. "Measuring nonfundamentalness for structural VARs," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 86-101.
    5. Luca Sala & Luca Gambetti & Mario Forni, 2016. "VAR Information and the Empirical Validation of DSGE Models," 2016 Meeting Papers 260, Society for Economic Dynamics.
    6. Silvia Miranda-Agrippino & Sinem Hacioglu Hoke & Kristina Bluwstein, 2018. "When Creativity Strikes: News Shocks and Business Cycle Fluctuations," Discussion Papers 1823, Centre for Macroeconomics (CFM).
    7. Ricco, Giovanni & Ellahie, Atif, 2012. "Government Spending Reloaded: Fundamentalness and Heterogeneity in Fiscal SVARs," MPRA Paper 42105, University Library of Munich, Germany.
    8. Angelini, Giovanni & Sorge, Marco M., 2021. "Under the same (Chole)sky: DNK models, timing restrictions and recursive identification of monetary policy shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    9. Fabio Canova & Mehdi Hamidi Sahneh, 2018. "Are Small-Scale SVARs Useful for Business Cycle Analysis? Revisiting Nonfundamentalness," Journal of the European Economic Association, European Economic Association, vol. 16(4), pages 1069-1093.
    10. Silvia Miranda Agrippino & Giovanni Ricco, 2018. "Identification with external instruments in structural VARs under partial invertibility," Working Papers hal-03475454, HAL.
    11. Giovanni Angelini & Luca Fanelli, 2016. "Misspecification and Expectations Correction in New Keynesian DSGE Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(5), pages 623-649, October.
    12. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2007. "A Review of Nonfundamentalness and Identification in Structural VAR Models," LEM Papers Series 2007/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. Alfred A. Haug & Christie Smith, 2012. "Local Linear Impulse Responses for a Small Open Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(3), pages 470-492, June.
    14. Ellahie, Atif & Ricco, Giovanni, 2017. "Government purchases reloaded: Informational insufficiency and heterogeneity in fiscal VARs," Journal of Monetary Economics, Elsevier, vol. 90(C), pages 13-27.
    15. Lanne, Markku & Saikkonen, Pentti, 2013. "Noncausal Vector Autoregression," Econometric Theory, Cambridge University Press, vol. 29(3), pages 447-481, June.
    16. Forni, Mario & Giannone, Domenico & Lippi, Marco & Reichlin, Lucrezia, 2009. "Opening The Black Box: Structural Factor Models With Large Cross Sections," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1319-1347, October.
    17. Paul Beaudry & Patrick Feve & Alain Guay & Franck Portier, 2019. "When is Nonfundamentalness in SVARs a Real Problem?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 34, pages 221-243, October.
    18. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2019. "Identification with External Instruments in Structural VARs under Partial Invertibility," The Warwick Economics Research Paper Series (TWERPS) 1213, University of Warwick, Department of Economics.
    19. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers 21/13, Institute for Fiscal Studies.
    20. Maddalena Cavicchioli, 2020. "Invertibility and VAR Representations of Time-Varying Dynamic Stochastic General Equilibrium Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 61-86, January.

    More about this item

    JEL classification:

    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ptu:wpaper:w202105. 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: DEE-NTD (email available below). General contact details of provider: https://edirc.repec.org/data/bdpgvpt.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.