IDEAS home Printed from https://ideas.repec.org/a/vrs/ekonom/v95y2016i2p7-29n1.html
   My bibliography  Save this article

A New Model with Regime Switching Errors: Forecasting Gdp in Times of Great Recession

Author

Listed:
  • Bartkus Algirdas

    (Vilnius University,Vilnius, Lithuania)

Abstract

This paper investigates the possibility to obtain better GDP forecasts in the early stages of Great Recession. Here, predictive performance refers to exclusively out-of-sample forecasts. Based on exploratory data analysis and general-to-specific modelling, this paper proposes a univariate predictive threshold model for the small open economy that outperforms its linear counterparts and correctly determines the course of events. This model does not explain any causal links; however, based on a set of economic arguments, it sets forward an idea regarding how a forecaster can act when principal determinant factors, responsible for a sudden, yet lasting change, are unknown, unmeasurable or cannot be influenced by national policy makers. A major dissimilarity between usual threshold models and the model presented in this paper is that while variables act differently under different conditions in the former, in this model, due to economic reasons, errors act differently. Alternatively, this paper can be viewed as a comparative GDP prediction study.

Suggested Citation

  • Bartkus Algirdas, 2016. "A New Model with Regime Switching Errors: Forecasting Gdp in Times of Great Recession," Ekonomika (Economics), Sciendo, vol. 95(2), pages 7-29, February.
    • Handle: RePEc:vrs:ekonom:v:95:y:2016:i:2:p:7-29:n:1
    DOI: 10.15388/ekon.2016.2.10122
    as

    Download full text from publisher

    File URL: https://doi.org/10.15388/ekon.2016.2.10122
    Download Restriction: no
    File URL: https://libkey.io/10.15388/ekon.2016.2.10122?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
    ---><---

    References listed on IDEAS

    as
    1. Ball, Laurence & Cecchetti, Stephen G, 1988. "Imperfect Information and Staggered Price Setting," American Economic Review, American Economic Association, vol. 78(5), pages 999-1018, December.
    2. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    3. Lars Ljungqvist & Thomas J. Sargent, 2004. "Recursive Macroeconomic Theory, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026212274x, December.
    4. Bernardini, Emmanuela & Cubadda, Gianluca, 2015. "Macroeconomic forecasting and structural analysis through regularized reduced-rank regression," International Journal of Forecasting, Elsevier, vol. 31(3), pages 682-691.
    5. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.
    6. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    7. Huber Florian, 2016. "Forecasting exchange rates using multivariate threshold models," The B.E. Journal of Macroeconomics, De Gruyter, vol. 16(1), pages 193-210, January.
    8. Barsoum, Fady & Stankiewicz, Sandra, 2015. "Forecasting GDP growth using mixed-frequency models with switching regimes," International Journal of Forecasting, Elsevier, vol. 31(1), pages 33-50.
    9. Whelan, Karl, 2014. "Ireland’s Economic Crisis: The Good, the Bad and the Ugly," Journal of Macroeconomics, Elsevier, vol. 39(PB), pages 424-440.
    10. Blanchard, Olivier J. & Romer, David & Spence, Michael & Stiglitz, Joseph E. (ed.), 2012. "In the Wake of the Crisis: Leading Economists Reassess Economic Policy," MIT Press Books, The MIT Press, edition 1, volume 1, number 026201761x, December.
    11. N. Gregory Mankiw, 1986. "The Allocation of Credit and Financial Collapse," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 101(3), pages 455-470.
    12. Böckers, Veit & Heimeshoff,Ulrich & Müller, Andrea, 2012. "Pull-forward effects in the German car scrappage scheme: A time series approach," DICE Discussion Papers 56, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    13. Kolasa, Marcin & Rubaszek, Michał, 2015. "Forecasting using DSGE models with financial frictions," International Journal of Forecasting, Elsevier, vol. 31(1), pages 1-19.
    14. D. COLANDER & al., 2010. "The Financial Crisis and the Systemic Failure of Academic Economics," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 6.
    15. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-2009 Recession," NBER Working Papers 18094, National Bureau of Economic Research, Inc.
    16. John B. Taylor, 2010. "Getting back on track: macroeconomic policy lessons from the financial crisis," Review, Federal Reserve Bank of St. Louis, vol. 92(May), pages 165-176.
    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. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does The Great Recession Imply The End Of The Great Moderation? International Evidence," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 745-760, April.
    2. Zanetti Chini, Emilio, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," International Journal of Forecasting, Elsevier, vol. 34(4), pages 711-732.
    3. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    4. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does The Great Recession Imply The End Of The Great Moderation? International Evidence," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 745-760, April.
    5. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    6. Benjamin Garcia & Arsenios Skaperdas, 2017. "Inferring the Shadow Rate from Real Activity," Finance and Economics Discussion Series 2017-106, Board of Governors of the Federal Reserve System (U.S.).
    7. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    8. Ana Beatriz Galvão & Michael T. Owyang, 2018. "Financial Stress Regimes and the Macroeconomy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(7), pages 1479-1505, October.
    9. Kurmaş Akdoğan, 2017. "Unemployment hysteresis and structural change in Europe," Empirical Economics, Springer, vol. 53(4), pages 1415-1440, December.
    10. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    11. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    12. Athreya, Kartik B., 2014. "Big Ideas in Macroeconomics: A Nontechnical View," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262019736, December.
    13. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    14. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    15. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    16. Steve Keen, 2013. "Predicting the ‘Global Financial Crisis’: Post-Keynesian Macroeconomics," The Economic Record, The Economic Society of Australia, vol. 89(285), pages 228-254, June.
    17. Cubadda, Gianluca & Guardabascio, Barbara, 2019. "Representation, estimation and forecasting of the multivariate index-augmented autoregressive model," International Journal of Forecasting, Elsevier, vol. 35(1), pages 67-79.
    18. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    19. Pirschel, Inske & Wolters, Maik, 2014. "Forecasting German key macroeconomic variables using large dataset methods," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100587, Verein für Socialpolitik / German Economic Association.
    20. Markus K. Brunnermeier & Thomas M. Eisenbach & Yuliy Sannikov, 2012. "Macroeconomics with Financial Frictions: A Survey," Levine's Working Paper Archive 786969000000000384, David K. Levine.

    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:vrs:ekonom:v:95:y:2016:i:2:p:7-29:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    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.