nep-for New Economics Papers
on Forecasting
Issue of 2012‒01‒10
three papers chosen by
Rob J Hyndman
Monash University

  1. Financial Variables and the Out-of-Sample Forecastability of the Growth Rate of Indian Industrial Production By Rangan Gupta; Yuxiang Ye; Christopher Sako; Tebatso Madisa
  2. Stock Return Predictability and Oil Prices By Jaime Casassus; Freddy Higuera
  3. A selecção de indicadores no estudo prospectivo “Forecasting the carbon footprint to road freight transport in 2020” [Indicator selection in the foresight study “Forecasting the carbon footprint to road freight transport in 2020”] By Nuno Boavida

  1. By: Rangan Gupta (Department of Economics, University of Pretoria); Yuxiang Ye (Department of Economics, University of Pretoria); Christopher Sako (Department of Economics, University of Pretoria); Tebatso Madisa (Department of Economics, University of Pretoria)
    Abstract: In this paper, we consider the forecasting power, both in- and out-of-sample, of 11 financial variables with respect to the growth rate of Indian industrial production over the monthly out-of-sample period of 2005:4-2011:4, using an in-sample of 1994:1-2005:3. The financial variables used are: M0, M1, M2, M3, lending rate, 3-month Treasury bill rate, term spread, real effective exchange rate, real stock prices, dividend yield and non-food credit growth. We observe that that in-sample and out-of-sample predictive ability of the financial variables tend to coincide. We find strong evidence of out-of-sample predictability for at least one of the horizons for M0, M1, M2, M3, the lending rate and real share price growth rate. The term-spread and dividend yield are added to the list when weaker versions of the out-of-sample test statistics are considered as well. Given that we consider a large number of financial variables, when we checked the significant results by accounting for data mining across the 11 financial variables, majority of these results ceases to be significant, with only M0, M1 and M2 retaining some of its predictive ability.
    Keywords: Nominal Financial Variables, Forecastablity, Forecast Encompassing, Industrial Production, India
    JEL: C22 C53 E44 E32
    Date: 2011–12
  2. By: Jaime Casassus; Freddy Higuera
    Abstract: This paper shows that oil price changes, measured as short-term futures returns, are a strong predictor of excess stock returns at short horizons. Ours is a leading variable for the business cycle and exhibits low persistence which avoids the ctitious long-horizon predictability associated to other predictors used in the literature. We compare our variable with the most popular predictors in a sample period that includes the recent nancial crisis. Our results suggest that oil price changes are the only variable with forecasting power for stock returns. This signi cant predictive ability is robust against the inclusion of other variables and out-of-sample tests. We also study the cross-section of expected stock returns in a conditional CAPM framework based on oil price shocks. Our model displays high statistical signi cance and a better t than all the conditional and unconditional models considered including the Fama French three-factor model. From a practical perspective, ours is a high-frequency, observable variable that has the advantage of being readily available to market-timing investors.
    Keywords: Return predictability, business cycle, crude oil, futures prices, asset pricing, conditional CAPM
    JEL: G17 E44 Q43 E32 G12 G14
    Date: 2011
  3. By: Nuno Boavida (IET, Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia)
    Abstract: This work examines a recent study that used various forecasting methods and in particular the Delphi method, to understand how the indicators were selected during the development of the prospective study. It can be concluded that the indicators in the study were selected through discussion on existing knowledge (formal and informal) and the broad consensus of the respective community, which established and confirmed the choice of indicators as the most relevant to prospectively examine the matter concerned. The technical support provided to choose certain forecasting methods as well as to choose the methods that could not be used throughout the development of the work, contributed to the strength of the list of indicators.
    Keywords: Indicators; forecasting; Delphi method; CO2 emissions; road freight transport; carbon footprint
    JEL: C18 C52 C53
    Date: 2011–06

This nep-for issue is ©2012 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.