nep-for New Economics Papers
on Forecasting
Issue of 2017‒07‒23
five papers chosen by
Rob J Hyndman
Monash University

  1. Integrating judgment in statistical demand forecasting: An approach to confront uncertainty By Niematallah Elamin; Mototsugu Fukushige
  2. World steel production: A new monthly indicator of global real economic activity By Ravazzolo, Francesco; Vespignani, Joaquin
  3. What Is the Expected Return on a Stock? By Christian Wagner; Ian Martin
  4. Forecasting the U.S. Real House Price Index By Vasilios Plakandaras; Rangan Gupta; Periklis Gogas; Theophilos Papadimitriou
  5. Forecast evaluation tests and negative long-run variance estimates in small samples By David I. Harvey; Stephen J. Leybourne; Emily J. Whitehouse

  1. By: Niematallah Elamin (Graduate School of Economics, Osaka University); Mototsugu Fukushige (Graduate School of Economics, Osaka University)
    Abstract: This paper investigates the potential value of judgment in forecasting demand after sudden changes in the external environment and in the presence of a high level of uncertainty. We forecast the daily load demand in Japan after the country fs 2011 severe energy crisis. The study examines statistical and judgmental techniques as competing or as complementary approaches, in the light of the availability of contextual information and relevant time-series data. The result indicates that immediately after a special event, the availability and dominance of contextual information seem to be the determinants of judgmental superiority over statistical models. However, when relevant time-series data are observed, statistical forecasting outperforms judgmental forecasting. When neither contextual information nor relevant time-series data is dominant, a combination of both methods succeeds in generating accurate forecasts. In addition, judgment is better in a combination framework than in the adjustment of statistical outputs.
    Keywords: Statistical forecasting, Judgmental forecasting, Combining forecasts, Adjusting forecasts, Contextual information, Forecast integration, Forecasting accuracy
    JEL: C53 Q4
    Date: 2017–07
  2. By: Ravazzolo, Francesco (Free University of Bozen/Bolzano, Italy); Vespignani, Joaquin (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: In this paper we propose a new indicator of monthly global real economic activity, named world steel production. We use world steel production, OECD industrial production index and Kilian’s rea index to forecast world real GDP, and key commodity prices. We find that world steel production generates large statistically significant gains in forecasting world real GDP and oil prices, relative to an autoregressive benchmark. A forecast combination of the three indices produces statistically significant gains in forecasting world real GDP, oil, natural gas, gold and fertilizer prices, relative to an autoregressive benchmark.
    Keywords: global real economic activity, world steel production, forecasting
    JEL: E1 E3 C1 C5 C8
    Date: 2017
  3. By: Christian Wagner (Copenhagen Business School); Ian Martin (London School of Economics)
    Abstract: We derive a formula that expresses the expected return on a stock in terms of the risk-neutral variance of the market and the stock's excess risk-neutral variance relative to the average stock. These components can be computed from index and stock option prices; the formula has no free parameters. We test the theory in-sample by running panel regressions of stock returns onto risk-neutral variances. The formula performs well at 6-month and 1-year forecasting horizons, and our predictors drive out beta, size, book-to-market, and momentum. Out-of-sample, we find that the formula outperforms a range of competitors in forecasting individual stock returns. Our results suggest that there is considerably more variation in expected returns, both over time and across stocks, than has previously been acknowledged.
    Date: 2017
  4. By: Vasilios Plakandaras; Rangan Gupta; Periklis Gogas; Theophilos Papadimitriou
    Abstract: The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.
    Date: 2017–07
  5. By: David I. Harvey; Stephen J. Leybourne; Emily J. Whitehouse
    Abstract: In this paper, we show that when computing standard Diebold-Mariano-type tests for equal forecast accuracy and forecast encompassing, the long-run variance can frequently be negative when dealing with multi-step-ahead predictions in small, but empirically relevant, sample sizes. We subsequently consider a number of alternative approaches to dealing with this problem, including direct inference in the problem cases and use of long-run variance estimators that guarantee positivity. The finite sample size and power of the different approaches are evaluated using extensive Monte Carlo simulation exercises. Overall, for multi-step-ahead forecasts, we find that the recently proposed Coroneo and Iacone (2016) test, which is based on a weighted periodogram long-run variance estimator, offers the best finite sample size and power performance.
    Keywords: Forecast evaluation; Long-run variance estimation; Simulation; Diebold-Mariano test; Forecasting JEL classification: C2

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