IDEAS home Printed from https://ideas.repec.org/p/ssb/dispap/562.html
   My bibliography  Save this paper

Classical identification: A viable road for data to inform structural modeling

Author

Listed:

Abstract

This paper addresses how to enhance the role of data in structural model design by utilizing structural breaks and superfluous information as auxiliary tools of exact identification. To illustrate the procedure and to study the simultaneous interplay between financial variables and the real side of the economy a simultaneous equation model is constructed on Norwegian aggregate data. In this model, while innovations to stock prices and credit do cause short run movements in real activity, such innovations do not precede real economy movements in the long run.

Suggested Citation

  • Roger Hammersland, 2008. "Classical identification: A viable road for data to inform structural modeling," Discussion Papers 562, Statistics Norway, Research Department.
  • Handle: RePEc:ssb:dispap:562
    as

    Download full text from publisher

    File URL: https://www.ssb.no/a/publikasjoner/pdf/DP/dp562.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beaudry, Paul & Portier, Franck, 2005. "The "news view" of economic fluctuations: Evidence from aggregate Japanese data and sectoral US data," Journal of the Japanese and International Economies, Elsevier, vol. 19(4), pages 635-652, December.
    2. Paul Beaudry & Franck Portier, 2006. "Stock Prices, News, and Economic Fluctuations," American Economic Review, American Economic Association, vol. 96(4), pages 1293-1307, September.
    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. Roger Hammersland & Dag Henning Jacobsen, 2008. "The Financial Accelerator: Evidence using a procedure of Structural Model Design," Discussion Papers 569, Statistics Norway, Research Department.

    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. Ippei Fujiwara & Yasuo Hirose & Mototsugu Shintani, 2011. "Can News Be a Major Source of Aggregate Fluctuations? A Bayesian DSGE Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 1-29, February.
    2. Kuan‐Jen Chen & Ching‐Chong Lai, 2015. "On‐the‐Job Learning and News‐Driven Business Cycles," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(2-3), pages 261-294, March.
    3. Markku Lanne & Helmut Luetkepohl, 2008. "Stock Prices and Economic Fluctuations: A Markov Switching Structural Vector Autoregressive Analysis," Economics Working Papers ECO2008/29, European University Institute.
    4. Féve, Patrick & Jidoud, Ahmat, 2012. "Identifying News Shocks from SVARs," Journal of Macroeconomics, Elsevier, vol. 34(4), pages 919-932.
    5. Pengfei Wang, 2012. "Understanding Expectation-Driven Fluctuations: A Labor-Market Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44, pages 487-506, March.
    6. Patrick Feve & Ahmat Jidoud, 2014. "News Shocks, Information Flows and SVARs," Annals of Economics and Statistics, GENES, issue 113-114, pages 293-307.
    7. Karnizova, Lilia, 2010. "The spirit of capitalism and expectation-driven business cycles," Journal of Monetary Economics, Elsevier, vol. 57(6), pages 739-752, September.
    8. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    9. Patrick Fève & Alain Guay, 2019. "Sentiments in SVARs," The Economic Journal, Royal Economic Society, vol. 129(618), pages 877-896.
    10. Lilia Karnizova, 2013. "Letting the speculative and the news views of the Japanese business cycle compete," Economics Bulletin, AccessEcon, vol. 33(2), pages 1146-1158.
    11. Yadav, Jayant, 2020. "Flight to Safety in Business cycles," MPRA Paper 104093, University Library of Munich, Germany.
    12. Nam, Deokwoo & Wang, Jian, 2014. "Are predictable improvements in TFP contractionary or expansionary: Implications from sectoral TFP?," Economics Letters, Elsevier, vol. 124(2), pages 171-175.
    13. Paul Beaudry & Martial Dupaigne & Franck Portier, 2011. "Modeling News-Driven International Business Cycles," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 72-91, January.
    14. Bibiana Lanzilotta Mernies, 2015. "Expectativas empresariales: consecuencias en el crecimiento en Uruguay," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, March.
    15. Hiroshi Morita, 2017. "Effects of Anticipated Fiscal Policy Shock on Macroeconomic Dynamics in Japan," The Japanese Economic Review, Springer, vol. 68(3), pages 364-393, September.
    16. Ko, Jun-Hyung & Miyazawa, Kensuke & Vu, Tuan Khai, 2012. "News shocks and Japanese macroeconomic fluctuations," Japan and the World Economy, Elsevier, vol. 24(4), pages 292-304.
    17. Roel van Elk & Marc van der Steeg & Dinand Webbink, 2013. "The effects of a special program for multi-problem school dropouts on educational enrolment, employment and criminal behaviour; Evidence from a field experiment," CPB Discussion Paper 241.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    18. Lucke, Bernd & Haertel, Thomas, 2008. "Do News Shocks Drive Business Cycles? Evidence from German Data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 2, pages 1-21.
    19. Fève, Patrick & Matheron, Julien & Sahuc, Jean-Guillaume, 2009. "On the dynamic implications of news shocks," Economics Letters, Elsevier, vol. 102(2), pages 96-98, February.
    20. Funashima, Yoshito & Iizuka, Nobuo & Ohtsuka, Yoshihiro, 2020. "GDP announcements and stock prices," Journal of Economics and Business, Elsevier, vol. 108(C).

    More about this item

    Keywords

    Structural vector Error Correction modeling; Identification; Cointegration; Financial variables and the real economy.;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - 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
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

    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:ssb:dispap:562. 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: L Maasø (email available below). General contact details of provider: https://edirc.repec.org/data/ssbgvno.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.