nep-for New Economics Papers
on Forecasting
Issue of 2017‒04‒02
five papers chosen by
Rob J Hyndman
Monash University

  1. The contribution of jumps to forecasting the density of returns By Christophe Chorro; Florian Ielpo; Benoît Sévi
  2. Does Inequality Help in Forecasting Equity Premium in a Panel of G7 Countries? By Christina Christou; Rangan Gupta; Fredj Jawadi
  3. A Predictive System for International Trade Growth By Chon, Sora
  4. Do central bank forecasts matter for professional forecasters? By Jacek Kotłowski
  5. Forecasting output gaps in the G-7 countries: The role of correlated Innovations and structural breaks By Dungey, Mardi; Jacobs, Jan P.A.M.; Tian, Jing

  1. By: Christophe Chorro (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Florian Ielpo (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Unigestion SA - UNIGESTION , IPAG Business School); Benoît Sévi (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - UN - Université de Nantes)
    Abstract: The extraction of the jump component in dynamics of asset prices haw witnessed a considerably growing body of literature. Of particular interest is the decomposition of returns' quadratic variation between their continuous and jump components. Recent contributions highlight the importance of this component in forecasting volatility at different horizons. In this article, we extend a methodology developed in Maheu and McCurdy (2011) to exploit the information content of intraday data in forecasting the density of returns at horizons up to sixty days. We follow Boudt et al. (2011) to detect intraday returns that should be considered as jumps. The methodology is robust to intra-week periodicity and further delivers estimates of signed jumps in contrast to the rest of the literature where only the squared jump component can be estimated. Then, we estimate a bivariate model of returns and volatilities where the jump component is independently modeled using a jump distribution that fits the stylized facts of the estimated jumps. Our empirical results for S&P 500 futures, U.S. 10-year Treasury futures, USD/CAD exchange rate and WTI crude oil futures highlight the importance of considering the continuous/jump decomposition for density forecasting while this is not the case for volatility point forecast. In particular, we show that the model considering jumps apart from the continuous component consistenly deliver better density forecasts for forecasting horizons ranging from 1 to 30 days.
    Keywords: leverage effect,density forecasting,jumps,realized volatility,bipower variation,median realized volatility
    Date: 2017–01
  2. By: Christina Christou (School of Economics and Management, Open University of Cyprus, Latsia, Cyprus); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Fredj Jawadi (University of Evry, Batiment La poste, Évry, France)
    Abstract: This paper investigates whether the post-tax and transfer growth rate in the Gini index can help in forecasting the equity premium in the G7 countries (Canada, France, Germany, Italy, Japan, United Kingdom (UK), and United States (US)). To this end, we use a panel data-based predictive framework, which controls for heterogeneity, cross-sectional dependence, persistence and endogeneity. When we analyze the annual out-of-sample period of 1990-2011, given an in-sample period of 1967-1989, our results show that: (a) Time series based predictive regression models fail to beat the benchmark of historical average, except for Italy; and, (b) the panel data models beat the benchmark in a statistically significant fashion for all the seven countries. Further, our results highlight the importance of pooling information when trying to forecast excess stock returns based on a measure of inequality.
    Keywords: Equity Premium, Inequality, G7 Countries, Panel Predictive Regressions
    JEL: C33 C53 G1
    Date: 2017–03
  3. By: Chon, Sora (Korea Institute for International Economic Policy)
    Abstract: The objective of this paper is to suggest a new predictive system for international trade, based on an unobserved component model. We employ the predictive system developed by Pastor and Stambaugh (2009), which is unlike other conventional predictive regression models. This paper derives an equivalent linear predictive regression from the predictive system, and explains why the proposed predictive system is able to achieve superior out-of-sample predictive power. When predictors are imperfect in an estimated equation, the equation fails to utilize all information from the predictors' past history, and unexplained variations are captured by residuals in the estimated equation. With the use of the predictive system, we can more effectively deal with the dynamics of imperfect predictors. For empirical illustration, we show that, in the case of Korea's export and import growth rates, the predictive system has better out-of-sample predictive powers than the conventional regressions based on Root Mean Squares Error (RMSE). Results from an out-of-sample analysis show that, compared to the benchmark model, the predictive system improves forecast precision by 18.90% for the export growth rate, and by 7.95% for the import growth rate.
    Keywords: Predictive System; Time-Series Analysis; Unobserved Component
    JEL: C22 C32 C53 F17
    Date: 2016–08–10
  4. By: Jacek Kotłowski
    Abstract: This paper examines to what extent public information provided by the central bank affects the forecasts formulated by professional forecasters. We investigated empirically whether disclosing GDP and inflation forecasts by Narodowy Bank Polski (the central bank of Poland) reduced the disagreement in professional forecasters' expectations. We also checked whether the strength of the forecasters’ reaction to the release of the central bank’s projection depends on the phase of the business cycle. Finally we identified the determinants of the dispersion among the forecasters.In the first step we used single equation models estimated separately for inflation and GDP forecasts. While the results confirm that by publishing its projection of future GDP growth, the central bank was reducing the dispersion of GDP forecasts, we extended the linear model and introduced asymmetry in the response of individual GDP forecasts to the release of NBP projection. Therefore in the second step we used the Smooth Transition Regression (STR) models with both logistic function and exponential transition function.The results only partially support the hypothesis on the coordinating role of the central bank existing in the literature. The main finding is that by publishing its projection of future GDP growth, the central bank was reducing the dispersion of one-year-ahead GDP forecasts. Our study indicates that the role of the central bank in reducing the forecasts dispersion was strengthening over time. We also found using non-linear STR models that the extent to which the projection release affected the dispersion of GDP forecasts varied over the business cycle. By disclosing its own projection the central bank reduced the disagreement among the forecasters the most in the periods when the economy moved from one phase of the business cycle to another.
    Keywords: Poland, Monetary issues, Forecasting and projection methods
    Date: 2015–07–01
  5. By: Dungey, Mardi (Tasmanian School of Business & Economics, University of Tasmania); Jacobs, Jan P.A.M. (; Tian, Jing (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: Trend GDP and output gaps play an important role in fiscal and monetary policy formulation, often including the need for forecasts. In this paper we focus on fore- casting trend GDP and output gaps with Beveridge-Nelson (1981) trend-cycle decompositions and investigate how these are affected by assumptions concern- ing correlated innovations and structural breaks. We evaluate expanding win- dows, one-step-ahead forecasts indirectly for the G-7 countries on the basis of real GDP growth rate forecasts. We find that correlated innovations affect real GDP growth rate forecasts positively, while allowing for structural breaks works for some countries but not for all. In the face of uncertainty the evidence supports that in making forecasts of trends and output gap policy makers should focus on allowing for the correlation of shocks as an order of priority higher than unknown structural breaks.
    Keywords: trend, output gap, trend-cycle decomposition, real GDP, forecasts, structural break
    JEL: C22 C53 C82
    Date: 2016

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