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Qual VAR Revisited: Good Forecast, Bad Story

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  • El-Shagi, Makram
  • von Schweinitz, Gregor

Abstract

Due to the recent financial crisis, the interest in econometric models that allow to incorporate binary variables (such as the occurrence of a crisis) experienced a huge surge. This paper evaluates the performance of the Qual VAR, i.e. a VAR model including a latent variable that governs the behavior of an observable binary variable. While we find that the Qual VAR performs reasonably well in forecasting (outperforming a probit benchmark), there are substantial identification problems. Therefore, when the economic interpretation of the dynamic behavior of the latent variable and the chain of causality matter, the Qual VAR is inadvisable.

Suggested Citation

  • El-Shagi, Makram & von Schweinitz, Gregor, 2012. "Qual VAR Revisited: Good Forecast, Bad Story," IWH Discussion Papers 12/2012, Halle Institute for Economic Research (IWH).
  • Handle: RePEc:zbw:iwhdps:iwh-12-12
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    1. Harding, Don & Pagan, Adrian, 2011. "An Econometric Analysis of Some Models for Constructed Binary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 86-95.
    2. Fornari, Fabio & Lemke, Wolfgang, 2010. "Predicting recession probabilities with financial variables over multiple horizons," Working Paper Series 1255, European Central Bank.
    3. Tobias Knedlik & Gregor Von Schweinitz, 2012. "Macroeconomic Imbalances as Indicators for Debt Crises in Europe," Journal of Common Market Studies, Wiley Blackwell, vol. 50(5), pages 726-745, September.
    4. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    5. Paap, Richard & Segers, Rene & van Dijk, Dick, 2009. "Do Leading Indicators Lead Peaks More Than Troughs?," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 528-543.
    6. Alessi, Lucia & Detken, Carsten, 2011. "Quasi real time early warning indicators for costly asset price boom/bust cycles: A role for global liquidity," European Journal of Political Economy, Elsevier, vol. 27(3), pages 520-533, September.
    7. 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.
    8. Eichengreen, Barry & Watson, Mark W & Grossman, Richard S, 1985. "Bank Rate Policy under the Interwar Gold Standard: A Dynamic Probit Model," Economic Journal, Royal Economic Society, vol. 95(379), pages 725-745, September.
    9. Heikki Kauppi & Pentti Saikkonen, 2008. "Predicting U.S. Recessions with Dynamic Binary Response Models," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 777-791, November.
    10. Henri Nyberg, 2010. "Dynamic probit models and financial variables in recession forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 215-230.
    11. Barrell, Ray & Davis, E. Philip & Karim, Dilruba & Liadze, Iana, 2010. "Bank regulation, property prices and early warning systems for banking crises in OECD countries," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2255-2264, September.
    12. Michael Dueker & Katrin Assenmacher-Wesche, 2010. "Forecasting macro variables with a Qual VAR business cycle turning point index," Applied Economics, Taylor & Francis Journals, vol. 42(23), pages 2909-2920.
    13. Rudebusch, Glenn D. & Williams, John C., 2009. "Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 492-503.
    14. Demirguc, Asli & Detragiache, Enrica, 2000. "Monitoring Banking Sector Fragility: A Multivariate Logit Approach," The World Bank Economic Review, World Bank Group, vol. 14(2), pages 287-307, May.
    15. Duttagupta, Rupa & Cashin, Paul, 2011. "Anatomy of banking crises in developing and emerging market countries," Journal of International Money and Finance, Elsevier, vol. 30(2), pages 354-376, March.
    16. Renee Fry & Adrian Pagan, 2007. "Some Issues in Using Sign Restrictions for Identifying Structural VARs," NCER Working Paper Series 14, National Centre for Econometric Research.
    17. Chauvet, Marcelle & Potter, Simon, 2010. "Business cycle monitoring with structural changes," International Journal of Forecasting, Elsevier, vol. 26(4), pages 777-793, October.
    18. Marcel Fratzscher, 2003. "On currency crises and contagion," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 8(2), pages 109-129.
    19. Fioramanti, Marco, 2008. "Predicting sovereign debt crises using artificial neural networks: A comparative approach," Journal of Financial Stability, Elsevier, vol. 4(2), pages 149-164, June.
    20. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
    21. Bussiere, Matthieu & Fratzscher, Marcel, 2006. "Towards a new early warning system of financial crises," Journal of International Money and Finance, Elsevier, vol. 25(6), pages 953-973, October.
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    Cited by:

    1. Michael W. McCracken & Joseph McGillicuddy & Michael T. Owyang, 2019. "Binary Conditional Forecasts," Working Papers 2019-029, Federal Reserve Bank of St. Louis, revised Apr 2021.
    2. Aßhoff, Sina & Belke, Ansgar & Osowski, Thomas, 2021. "Unconventional monetary policy and inflation expectations in the Euro area," Economic Modelling, Elsevier, vol. 102(C).

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    More about this item

    Keywords

    binary choice model; Gibbs sampling; latent variable; MCMC; method evaluation; Binary-Choice-Modelle; Gibbs-Sampling; latente Variable; MCMC; Methodenevaluation;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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