IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v3y2015i3p381-392d54168.html
   My bibliography  Save this article

A Probit Model for the State of the Greek GDP Growth

Author

Listed:
  • Stavros Degiannakis

    (Department of Economics and Regional Development, Panteion University, 136 Syngrou Avenue, GR17671 Athens, Greece
    Postgraduate Department of Business Administration, Hellenic Open University, Aristotelous 18, GR26335 Thessaloniki, Greece)

Abstract

The paper provides probability estimates of the state of the GDP growth. A regime-switching model defines the probability of the Greek GDP being in boom or recession. Then probit models extract the predictive information of a set of explanatory (economic and financial) variables regarding the state of the GDP growth. A contemporaneous, as well as a lagged, relationship between the explanatory variables and the state of the GDP growth is conducted. The mean absolute distance (MAD) between the probability of not being in recession and the probability estimated by the probit model is the function that evaluates the performance of the models. The probit model with the industrial production index and the realized volatility as the explanatory variables has the lowest MAD value of 6.43% (7.94%) in the contemporaneous (lagged) relationship.

Suggested Citation

  • Stavros Degiannakis, 2015. "A Probit Model for the State of the Greek GDP Growth," IJFS, MDPI, vol. 3(3), pages 1-12, August.
    • Handle: RePEc:gam:jijfss:v:3:y:2015:i:3:p:381-392:d:54168
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/3/3/381/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/3/3/381/
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Stavros Degiannakis, George Filis, and Renatas Kizys, 2014. "The Effects of Oil Price Shocks on Stock Market Volatility: Evidence from European Data," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    2. Ang, Andrew & Bekaert, Geert, 2002. "Regime Switches in Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 163-182, April.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    4. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    5. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    6. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    7. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, April.
    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. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2012. "Combination schemes for turning point predictions," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 402-412.
    2. Shaw, Charles, 2018. "Regime-Switching And Levy Jump Dynamics In Option-Adjusted Spreads," MPRA Paper 94154, University Library of Munich, Germany, revised 27 May 2019.
    3. Fiorentini, Gabriele & Planas, Christophe & Rossi, Alessandro, 2016. "Skewness and kurtosis of multivariate Markov-switching processes," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 153-159.
    4. Pardo, S. & Rautureau, N. & Vallée, T., 2011. "Optimal versus realized policy rules in a regime-switching framework," Economic Modelling, Elsevier, vol. 28(6), pages 2761-2775.
    5. Goutte, Stéphane, 2014. "Conditional Markov regime switching model applied to economic modelling," Economic Modelling, Elsevier, vol. 38(C), pages 258-269.
    6. Duncan Fong & Wayne DeSarbo, 2007. "A Bayesian methodology for simultaneously detecting and estimating regime change points and variable selection in multiple regression models for marketing research," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 427-453, December.
    7. Kim, Chang-Jin & Morley, James C. & Nelson, Charles R., 2001. "Does an intertemporal tradeoff between risk and return explain mean reversion in stock prices?," Journal of Empirical Finance, Elsevier, vol. 8(4), pages 403-426, September.
    8. Omokolade Akinsomi & Mehmet Balcilar & Rıza Demirer & Rangan Gupta, 2017. "The effect of gold market speculation on REIT returns in South Africa: a behavioral perspective," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 41(4), pages 774-793, October.
    9. Grace Lee, 2011. "Aggregate shocks decomposition for eight East Asian countries," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 16(2), pages 215-232.
    10. Shanshan Qin & Zhenni Tan & Yuehua Wu, 2024. "On robust estimation of hidden semi-Markov regime-switching models," Annals of Operations Research, Springer, vol. 338(2), pages 1049-1081, July.
    11. Philip Kostov & John Lingard, 2004. "Regime-switching Vector Error Correction Model (VECM) analysis of UK meat consumption," Econometrics 0409007, University Library of Munich, Germany.
    12. Muriel Nguiffo-Boyom, 2006. "Un indicateur de retournement conjoncturel pour la France : une application du modèle à facteur avec changements de régimes," Économie et Prévision, Programme National Persée, vol. 172(1), pages 101-114.
    13. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Advances in Econometrics, in: Missing Data Methods: Time-Series Methods and Applications, pages 1-86, Emerald Group Publishing Limited.
    14. Smith, Aaron & Naik, Prasad A. & Tsai, Chih-Ling, 2006. "Markov-switching model selection using Kullback-Leibler divergence," Journal of Econometrics, Elsevier, vol. 134(2), pages 553-577, October.
    15. Andreas Graflund & Birger Nilsson, 2003. "Dynamic Portfolio Selection: the Relevance of Switching Regimes and Investment Horizon," European Financial Management, European Financial Management Association, vol. 9(2), pages 179-200, June.
    16. AUGUSTYNIAK, Maciej & BAUWENS, Luc & DUFAYS, Arnaud, 2016. "A New Approach to Volatility Modeling : The High-Dimensional Markov Model," LIDAM Discussion Papers CORE 2016042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    17. Nilsson, Birger, 2002. "Financial Liberalization and the Changing Characteristics of Nordic Stock Returns," Working Papers 2002:4, Lund University, Department of Economics.
    18. Angelidis, Timotheos & Degiannakis, Stavros & Filis, George, 2015. "US stock market regimes and oil price shocks," Global Finance Journal, Elsevier, vol. 28(C), pages 132-146.
    19. Joao Leitao & Joaquim Ferreira & Ernesto Santibanez‐Gonzalez, 2021. "Green bonds, sustainable development and environmental policy in the European Union carbon market," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 2077-2090, May.
    20. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," ifo Working Paper Series 3, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

    More about this item

    Keywords

    GDP growth; industrial production; probability of recession; probit model; realized volatility; regime switching;
    All these keywords.

    JEL classification:

    • 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
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - 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
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

    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:gam:jijfss:v:3:y:2015:i:3:p:381-392:d:54168. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.