nep-for New Economics Papers
on Forecasting
Issue of 2018‒03‒05
six papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Industrial Production and Inflation in Turkey with Factor Models By Mahmut Gunay
  2. The Predictive Value of Inequality Measures for Stock Returns: An Analysis of Long-Span UK Data Using Quantile Random Forests By Rangan Gupta; Christian Pierdzioch; Andrew J. Vivian; Mark E. Wohar
  3. Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes By Ricardo Crisostomo; Lorena Couso
  4. Why are inflation forecasts sticky? By Frédérique BEC
  5. Oil Price Shocks and Economic Growth: The Volatility Link By John M. Maheu; Yong Song; Qiao Yang
  6. Stock returns forecast: an examination by means of Artificial Neural Networks By Martin Iglesias Caride; Aurelio F. Bariviera; Laura Lanzarini

  1. By: Mahmut Gunay
    Abstract: In this paper, industrial production growth and core inflation are forecasted using a large number of domestic and international indicators. Two methods are employed, factor models and forecast combination, to deal with the curse of dimensionality problem stemming from the availability of ever growing data sets. A comprehensive analysis is carried out to understand the sensitivity of the forecast performance of factor models to various modelling choices. In this respect, effects of factor extraction method, number of factors, data aggregation level and forecast equation type on the forecasting performance are analyzed. Moreover, the effect of using certain data blocks such as European Union variables and interest rates on the forecasting performance is evaluated as well. Out-of-sample forecasting exercise is conducted for two consecutive periods to assess the stability of the forecasting performance. Results show that best performing specifications depend on the type of the variable that one wants to forecast, the forecast horizon and the sample period used to evaluate the out-of-sample forecasting performance. Factor models perform better than the combination of bi-variate forecasts.
    Keywords: Forecasting, Factor models, Principal component
    JEL: E37 C53
    Date: 2018
  2. By: Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Hamburg, Germany); Andrew J. Vivian (School of Business and Economics, Loughborough University, Leicestershire, UK); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, Omaha, USA and School of Business and Economics, Loughborough University, Leicestershire, UK)
    Abstract: We contribute to research on the predictability of stock returns in two ways. First, we use quantile random forests to study the predictive value of the various inequality measures across the quantiles of the conditional distribution of stock returns. Second, we examine whether various measures of consumption-based and income-based inequality, measured at a quarterly frequency, have out-of-sample predictive value for stock returns at various forecast horizons. Our results suggest that the inequality measures being studied have predictive value for stock returns in sample, but do not systematically predict stock returns out of sample.
    Keywords: Stock returns, Predictability, Inequality measures, Quantile random forests
    JEL: C53 G17
    Date: 2018–02
  3. By: Ricardo Crisostomo; Lorena Couso
    Abstract: We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the usual caveats of probabilistic forecasts studies -small samples, limited models and non-holistic validations- by performing a comprehensive comparison of 15 predictive schemes during a time period of over 21 years. All densities are evaluated in terms of their statistical consistency, local accuracy and forecasting errors. Using a new indicator, the Integrated Forecast Score (IFS), we show that risk-neutral densities outperform historical-based predictions in terms of information content. We find that the Variance Gamma model generates the highest out-of-sample likelihood of observed prices and the lowest predictive errors, whereas the ARCH-based GRJ-FHS delivers the most consistent forecasts across the entire density range. In contrast, lognormal densities, the Heston model or the Breeden-Litzenberger formula yield biased predictions and are rejected in statistical tests.
    Date: 2018–01
  4. By: Frédérique BEC (Université de Cergy-Pontoise, THEMA)
    Abstract: This paper proposes a theoretical model of forecasts formation which implies that in presence of information observation and forecasts communication costs, rational professional forecasters might find it optimal not to revise their forecasts continuously, or at any time. The threshold time- and state-dependence of the observation reviews and forecasts revisions implied by this model are then tested using inflation forecast updates of professional forecasters from recent Consensus Economics panel data for France and Germany. Our empirical results support the presence of both kinds of dependence, as well as their threshold-type shape. They also imply an upper bound of the optimal time between two information observations of about six months and the co-existence of both types of costs, the observation cost being about 1.5 times larger than the communication cost.
    Keywords: Forecast revision, binary choice models, information and communication costs.
    JEL: C23 D8 E31
    Date: 2017
  5. By: John M. Maheu (DeGroote School of Business, McMaster University, Canada; Rimini Centre for Economic Analysis); Yong Song (University of Melbourne, Australia; Rimini Centre for Economic Analysis); Qiao Yang (School of Entrepreneurship and Management, ShanghaiTech University, China)
    Abstract: This paper shows that oil shocks primarily impact economic growth through the conditional variance of growth. We move beyond the literature that focuses on conditional mean point forecasts and compare models based on density forecasts. Over a range of dynamic models, oil shock measures and data we find a robust link between oil shocks and the volatility of economic growth. A new measure of oil shocks is developed and shown to be superior to existing measures and indicates that the conditional variance of growth increases in response to an indicator of local maximum oil price exceedance. The empirical results uncover a large pronounced asymmetric response of growth volatility to oil price changes. Uncertainty about future growth is considerably lower compared to a benchmark AR(1) model when no oil shocks are present.
    Keywords: Bayes factors, predictive likelihoods, nonlinear dynamics, density forecast
    JEL: C53 C32 C11 Q43
    Date: 2018–02
  6. By: Martin Iglesias Caride; Aurelio F. Bariviera; Laura Lanzarini
    Abstract: The validity of the Efficient Market Hypothesis has been under severe scrutiny since several decades. However, the evidence against it is not conclusive. Artificial Neural Networks provide a model-free means to analize the prediction power of past returns on current returns. This chapter analizes the predictability in the intraday Brazilian stock market using a backpropagation Artificial Neural Network. We selected 20 stocks from Bovespa index, according to different market capitalization, as a proxy for stock size. We find that predictability is related to capitalization. In particular, larger stocks are less predictable than smaller ones.
    Date: 2018–01

This nep-for issue is ©2018 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.