nep-for New Economics Papers
on Forecasting
Issue of 2017‒09‒10
four papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting growth of U.S. aggregate and household-sector M2 after 2000 using economic uncertainty measures By Artur Tarassow
  2. Forecasting the return volatility of European equity markets under different market conditions:A GARCH-MIDAS approach By Afees A. Salisu; Umar N. Ndako
  3. Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors By Clark, Todd E.; McCracken, Michael W.; Mertens, Elmar
  4. Forecasting the return volatility of energy prices: A GARCH MIDAS approach By Afees A. Salisu; Raymond Swaray

  1. By: Artur Tarassow (Universität Hamburg (University of Hamburg))
    Abstract: This paper evaluates the predictive out-of-sample forecasting properties of six different economic uncertainty variables for both growth in aggregate M2 and growth in household-sector M2 in the U.S. using data between 1971m1 and 2014m12. The core contention is that economic uncertainty improves both forecast accuracy as well as direction-of-change forecasts of real money stock growth. We estimate linear ARDL models using the iterated rolling-window forecasting scheme combined with two different indicator selection procedures. Forecast accuracy is evaluated by RMSE and the Diebold-Mariano test. Direction-of-change forecasts are assessed by means of the Kuipers Score and the Pesaran-Timmermann test. The results indicate an increased relevance of certain economic uncertainty measures for forecasting growth in both real aggregate as well as real household-sector M2 since 2000.
    Keywords: Money demand, uncertainty, risk, multi-step forecasts, forecast comparison
    JEL: C22 E41 E47
    Date: 2017–08
  2. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan); Umar N. Ndako (Monetary Policy Department, Central Bank of Nigeria, Nigeria.)
    Abstract: In this paper, we employ the GARCH-MIDAS modelling framework to forecast the return volatility of the European equity markets on the basis of the predictive powers of such macroeconomic information as realised volatility, the level of economic activities and macroeconomic uncertainty. We distinctly evaluate the behaviour of the return volatilities under different market conditions designated as „Pre Euro Regime,‟ „Euro /Pre-GFC Regime,‟ and „Euro/Post-GFC Regime‟. Our findings show that the macroeconomic information considered in the model are good predictors of the return volatility of the European equity markets. Also, the in-sample and out-of-sample forecast results of these predictors are sensitive to data sample and the market conditions.
    Keywords: FIFA, World cup, Second round qualification, Binary Choice Model (BCM)
    JEL: C58 F37 G17
    Date: 2017–09
  3. By: Clark, Todd E. (Federal Reserve Bank of Cleveland); McCracken, Michael W. (Federal Reserve Bank of St. Louis); Mertens, Elmar (Bank for International Settlements)
    Abstract: We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee's Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. Compared to constant-variance approaches, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts.
    Keywords: Stochastic volatility; survey forecasts; prediction
    JEL: C32 C53 E47
    Date: 2017–08–28
  4. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan); Raymond Swaray (Economics Subject Group, University of Hull Business, University of Hull, Cottingham Road, UK)
    Abstract: This paper offers an extension to the literature on energy prices by forecasting the return volatility of these prices using the GARCH-MIDAS approach. In addition to the realized volatility, it also evaluates the predictability of relevant macroeconomic information such as industrial growth and consumer prices (with and without energy components) in the predictive model for the return volatility of energy prices. The analyses are distinctly conducted for full-sample, pre-GFC and post-GFC periods. On average, the findings support the inclusion of these macroeconomic information particularly output growth and realized volatility as they yield good in-sample and outof- sample predictability results for the return volatility. However, the paper finds contrasting evidence between the pre-GFC and post-GFC periods.
    Keywords: GARCH-MIDAS; energy prices; return volatility; realized volatility, industrial production, inflation
    JEL: C53 Q47
    Date: 2017–09

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