nep-for New Economics Papers
on Forecasting
Issue of 2018‒09‒10
seven papers chosen by
Rob J Hyndman
Monash University

  1. Selection of calibration windows for day-ahead electricity price forecasting By Grzegorz Marcjasz; Tomasz Serafin; Rafal Weron
  2. Economic significance of commodity return forecasts from the fractionally cointegrated VAR model By Dolatabadi, Sepideh; Kumar Narayan, Paresh; Orregaard Nielsen, Morten; Xu, Ke
  3. Application of Garch Models to Estimate and Predict Financial Volatility of Daily Stock Returns in Nigeria By Ekong, Christopher N.; Onye, Kenneth U.
  4. Forecasting Nigerian Inflation using Model Averaging methods: Modelling Frameworks to Central Banks By Tumala, Mohammed M; Olubusoye, Olusanya E; Yaaba, Baba N; Yaya, OlaOluwa S; Akanbi, Olawale B
  5. Forecasting with Many Predictors: How Useful are National and International Confidence Data? By Kevin Moran; Simplice Aimé Nono; Imad Rherrad
  6. Model Selection in Factor-Augmented Regressions with Estimated Factors By Djogbenou, Antoine A.
  7. Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons By Chevillon, Guillaume

  1. By: Grzegorz Marcjasz; Tomasz Serafin; Rafal Weron
    Abstract: We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves 6-year long datasets from three major power markets and four autoregressive expert models fitted either to raw or transformed prices. Since the variability of prediction errors across windows of different lengths and across datasets can be substantial, selecting ex-ante one window is risky. Instead, we argue that averaging forecasts across different calibration windows is a robust alternative and introduce a new, well-performing weighting scheme for averaging these forecasts.
    Keywords: Electricity price forecasting; Forecast averaging; Calibration window; Autoregression; Variance stabilizing transformation; Conditional predictive ability
    JEL: C14 C22 C51 C53 Q47
    Date: 2018–08–29
  2. By: Dolatabadi, Sepideh; Kumar Narayan, Paresh; Orregaard Nielsen, Morten; Xu, Ke
    Abstract: Based on recent evidence of fractional cointegration in commodity spot and futures mar- kets, we investigate whether a fractionally cointegrated model can provide statistically and/or economically signicant forecasts of commodity returns. Specically, we propose to model and forecast commodity spot and futures prices using a fractionally cointegrated vector autoregres- sive (FCVAR) model that generalizes the more well-known (non-fractional) CVAR model to allow fractional integration. We derive the best linear predictor for the FCVAR model and perform an out-of-sample forecast comparison with the non-fractional model. In our empirical analysis to daily data on 17 commodity markets, the fractional model is found to be superior in terms of in-sample t and also out-of-sample forecasting based on statistical metrics of forecast comparison. We analyze the economic signicance of the forecasts through a dynamic trading strategy based on a portfolio with weights derived from a mean-variance utility function. Al- though there is much heterogeneity across commodity markets, this analysis leads to statistically signicant and economically meaningful prots in most markets, and shows that prots from both the fractional and non-fractional models are higher on average and statistically more signif- icant than prots derived from a simple moving-average strategy. The analysis also shows that, in spite of the statistical advantage of the fractional model, the fractional and non-fractional models generate very similar prots with only a slight advantage to the fractional model on average.
    Keywords: Financial Economics, Marketing
    Date: 2017–01
  3. By: Ekong, Christopher N.; Onye, Kenneth U.
    Abstract: This paper estimates the optimal forecasting model of stock returns and the nature of stock returns volatility in Nigeria using daily All-Share stock data. The study unlike previous ones estimates six sets of symmetric and asymmetric GARCH-family models of stock returns volatility (three of which are augmented with trading volume) in three different set of error distributions: normal, student’s t and generalized error distribution (GED) with a view to selecting the model with best predictive power. Relying on root mean square error (RMSE) and Thiel’s Inequality Coefficient, GARCH (1,1) and augmented EGARCH(1,1) in GED proved to possess the best forecasting capability as adjudged by the last 30 days out-of-sample forecast. Our finding also suggests the presence of leverage effect and decline in persistence parameter after incorporating trading volume. Overall, the result provides evidence of high probability of making negative return from investment in the Nigerian stock market over the sample period. The empirical merit of the model is, thus, its potential for applications in analysis of value at risk (VAR) of quoted stocks and, therefore, evaluation of risk premia that guide investors’ choice of stock portfolio.
    Keywords: Stock Returns, Forecasting, GARCH Model, Nigeria
    JEL: E17 G1 G12 G17
    Date: 2017
  4. By: Tumala, Mohammed M; Olubusoye, Olusanya E; Yaaba, Baba N; Yaya, OlaOluwa S; Akanbi, Olawale B
    Abstract: As a result of the adverse macroeconomic effect of inflation on welfare, fiscal budgeting, trade performance, international competitiveness and the whole economy, inflation still remains a subject of utmost concern and interest to policy makers. The traditional Philips curve as well as other methodologies have been criticized for their inability to track correctly the pattern of inflation, particularly, these models do not allow for enough variables to be included as part of the regressors, and judgment is often made by a single model. In this work, model averaging techniques via Bayesian and frequentist approach were considered. Specifically, we considered the Bayesian model averaging (BMA) and Frequentist model averaging (FMA) techniques to model and forecast future path of CPI inflation in Nigeria using a wide range of variables. The results indicated that both in-sample and out-of-sample forecasts were highly reliable, judging from the various forecast performance criteria. Various policy scenarios conducted were highly fascinating both from the theoretical perspective and the prevailing economic situation in the country.
    Keywords: Bayesian model averaging; Forecasting; Frequentist approach; Inflation rate; Nigeria
    JEL: C30 C32 C5
    Date: 2017–12
  5. By: Kevin Moran; Simplice Aimé Nono; Imad Rherrad
    Abstract: This paper assesses the contribution of Canadian and International (US) confidence data, drawn from consumer and business sentiment surveys, for forecasting Canadian GDP growth. The targeting approaches of Bai and Ng (2008) and Bai and Ng (2009) are employed to extract promising predictors from large databases each containing between several dozen and several hundred time series. The databases are categorised between those containing macroeconomic (Canadian and US) and confidence (Canadian and US) data, allowing us to assess the specific value added of international and confidence data. We find that forecasting ability is consistently improved by considering information from national confidence data; by contrast, their US counterparts appear to be helpful only when combined with national time-series. Overall, most relevant gains in forecasting performance are observed for short-term (up to threequarters-ahead) horizons, perhaps reflecting the timing advantage in the releases of sentiment data.
    Date: 2018
  6. By: Djogbenou, Antoine A.
    Abstract: This paper proposes two consistent model selection procedures for factor-augmented regressions in finite samples. We first demonstrate that the usual cross-validation is inconsistent, but that a generalization, leave-d-out cross-validation, selects the smallest basis for the space spanned by the true factors. The second proposed criterion is a generalization of the bootstrap approximation of the squared error of prediction of Shao (1996) to factor-augmented regressions. We show that this procedure is consistent. Simulation evidence documents improvements in the probability of selecting the smallest set of estimated factors than the usually available methods. An illustrative empirical application that analyzes the relationship between expected stock returns and factors extracted from a large panel of United States macroeconomic and financial data is conducted. Our new procedures select factors that correlate heavily with interest rate spreads and with the Fama-French factors. These factors have strong predictive power for excess returns.
    Keywords: Financial Economics
    Date: 2017–10
  7. By: Chevillon, Guillaume (ESSEC Research Center, ESSEC Business School)
    Abstract: This paper studies the properties of multi-step projections, and forecasts that are obtained using either iterated or direct methods. The models considered are local asymptotic: they allow for a near unit root and a local to zero drift. We treat short, intermediate and long term forecasting by considering the horizon in relation to the observable sample size. We show the implication of our results for models of predictive regressions used in the financial literature. We show here that direct projection methods at intermediate and long horizons are robust to the potential misspecification of the serial correlation of the regression errors. We therefore recommend, for better global power in predictive regressions, a combination of test statistics with and without autocorrelation correction.
    Keywords: Multi-step Forecasting; Predictive Regressions; Local Asymptotics; Dynamic Misspecification; Finite Samples; Long Horizons
    JEL: C22 C52 C53
    Date: 2017–07–23

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.