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Joint Prediction Bands for Macroeconomic Risk Management

Author

Listed:
  • Farooq Akram

  • Andrew Binning

  • Junior Maih

Abstract

In this paper we address the issue of assessing and communicating the joint probabilities implied by density forecasts from multivariate time series models. We focus our attention in three areas. First, we investigate a new method of producing fan charts that better communicates the uncertainty present in forecasts from multivariate time series models. Second, we suggest a new measure for assessing the plausibility of non-central point forecasts. And third, we describe how to use the density forecasts from a multivariate time series model to assess the probability of a set of future events occurring. An additional novelty of this paper is our use of a regime-switching DSGE model with an occasionally binding zero lower bound constraint, estimated on US data, to produce the density forecasts. The tools we offer will allow practitioners to better assess and communicate joint forecast probabilities, a criticism that has been leveled at central bank communications.

Suggested Citation

  • Farooq Akram & Andrew Binning & Junior Maih, 2016. "Joint Prediction Bands for Macroeconomic Risk Management," Working Papers No 5/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  • Handle: RePEc:bny:wpaper:0045
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    References listed on IDEAS

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    1. Andrew Binning & Junior Maih, 2016. "Implementing the zero lower bound in an estimated regime-switching DSGE model," Working Paper 2016/3, Norges Bank.
    2. Robertson, John C & Tallman, Ellis W & Whiteman, Charles H, 2005. "Forecasting Using Relative Entropy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 383-401, June.
    3. Junior Maih, 2010. "Conditional forecasts in DSGE models," Working Paper 2010/07, Norges Bank.
    4. Michal Franta & Jozef Baruník & Roman Horváth & Katerina Smídková, 2014. "Are Bayesian Fan Charts Useful? The Effect of Zero Lower Bound and Evaluation of Financial Stability Stress Tests," International Journal of Central Banking, International Journal of Central Banking, vol. 10(1), pages 159-188, March.
    5. Leeper, Eric M. & Zha, Tao, 2003. "Modest policy interventions," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1673-1700, November.
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    9. M. Hashem Pesaran, 2000. "Forecast Uncertainties in Macroeconometric Modelling: An Application to the UK Economy," CESifo Working Paper Series 345, CESifo.
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    11. Diebold, Francis X. & Schorfheide, Frank & Shin, Minchul, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 201(2), pages 322-332.
    12. Ragna Alstadheim & Hilde C. Bjørnland & Junior Maih, 2013. "Do Central Banks Respond to Exchange Rate Movements? A Markow-Switching Structural Investigation," Working Papers No 9/2013, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
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    15. Hugo Gerard & Kristoffer Nimark, 2008. "Combining Multivariate Density Forecasts Using Predictive Criteria," RBA Research Discussion Papers rdp2008-02, Reserve Bank of Australia.
    16. Dag Kolsrud, 2015. "A Time‐Simultaneous Prediction Box for a Multivariate Time Series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(8), pages 675-693, December.
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    18. Michael Wolf & Dan Wunderli, 2012. "Bootstrap joint prediction regions," ECON - Working Papers 064, Department of Economics - University of Zurich, revised May 2013.
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    3. Syed Tehseen Jawaid, Abdul Waheed, 2017. "Uncertainty and Risk Analysis of Pakistan's Regional Trade: Fan Chart Approach," Journal of Management Sciences, Geist Science, Iqra University, Faculty of Business Administration, vol. 4(1), pages 55-81, March.

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    Keywords

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    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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