nep-for New Economics Papers
on Forecasting
Issue of 2014‒01‒17
ten papers chosen by
Rob J Hyndman
Monash University

  1. Can the Sharia-Based Islamic Stock Market Returns be Forecasted Using Large Number of Predictors and Models? By Rangan Gupta; Shawkat Hammoudeh; Beatrice D. Simo-Kengne; Soodabeh Sarafrazi
  2. The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time By A. Girardi; R. Golinelli; C. Pappalardo
  3. Testing the Out-of-Sample Forecasting Ability of a Financial Conditions Index for South Africa By Kirsten Thompson; Renee van Eyden; Rangan Gupta
  4. Modeling the impact of forecast-based regime switches on macroeconomic time series By Bel, K.; Paap, R.
  5. Some Tools for Robustifying Econometric Analyses By Hoornweg, V.
  6. The impact of forecasting errors on warehouse labor efficiency: A case study in consumer electronics By Kim, T.Y.; Dekker, R.; Heij, C.
  7. How to Identify and Forecast Bull and Bear Markets? By Kole, H.J.W.G.; van Dijk, D.J.C.
  8. Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting By Tao Xiong; Yukun Bao; Zhongyi Hu
  9. What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance? By Chang, C-L.; McAleer, M.J.
  10. Risk Modelling and Management: An Overview By Chang, C-L.; Allen, D.E.; McAleer, M.J.; Pérez-Amaral, T.

  1. By: Rangan Gupta (Department of Economics, University of Pretoria); Shawkat Hammoudeh (Lebow College of Business, Drexel University, Philadelphia, USA); Beatrice D. Simo-Kengne (Department of Economics, University of Pretoria); Soodabeh Sarafrazi (Lebow College of Business, Drexel University, Philadelphia, USA)
    Abstract: This study employs fourteen global economic and financial variables to predict the return of the Islamic stock market as identified by the Dow Jones Islamic stock market. It implements alternative forecasting methods and allows for nonlinearity in the multivariate predictive regressions by estimating time-varying parameter models. All the methods fail to forecast the returns of the Sharia-based DJIM index over the out-of-sample period. The forecasts are weak at best, with only four predictors the three-month Treasury bill rate, inflation, oil price and return on the S&P500 index outperforming the benchmark autoregressive model of order one. The study suggests that the DJIM return is best predicted by an AR(1) model, and that future research should aim at analysing whether the performance of the linear autoregressive model can be improved by using nonlinear methods.
    Keywords: DJIM, forecasting methods, out-of-sample forecasts, benchmark model
    JEL: C58 G11
    Date: 2013–12
  2. By: A. Girardi; R. Golinelli; C. Pappalardo
    Abstract: Building on the literature on regularization and dimension reduction methods, we have developed a quarterly forecasting model for euro area GDP. This method consists in bridging quarterly national accounts data using factors extracted from a large panel of monthly and quarterly series including business surveys and financial indicators. The pseudo real-time nature of the information set is accounted for as the pattern of publication lags is considered. Forecast evaluation exercises show that predictions obtained through various dimension reduction methods outperform both the benchmark AR and the diffusion index model without pre-selected indicators. Moreover, forecast combination significantly reduces forecast error.
    JEL: C53 C22 E37 F47
    Date: 2014–01
  3. By: Kirsten Thompson (Department of Economics, University of Pretoria); Renee van Eyden (Department of Economics, University of Pretoria); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: The importance of financial instability for the world economy has been severely demonstrated since the 2007/08 global financial crisis, highlighting the need for a better understanding of financial conditions. We use a financial conditions index (FCI) for South Africa previously constructed from 16 financial variables to test whether the rolling-window estimated FCI does better than its individual financial components in forecasting key macroeconomic variables, such as output growth, inflation and interest rates. The concept of forecast encompassing is used to examine the forecasting ability of these variables controlling for data-mining. We find that the rolling-window estimated FCI has out-of-sample forecasting ability with respect to manufacturing output growth at the one, three and six month horizons, but has no forecasting ability with respect to inflation and interest rates.
    Keywords: Financial conditions index, forecast encompassing, data-mining, financial crisis
    JEL: C22 C53 G01
    Date: 2013–12
  4. By: Bel, K.; Paap, R.
    Abstract: Forecasts of key macroeconomic variables may lead to policy changes of governments, central banks and other economic agents. Policy changes in turn lead to structural changes in macroeconomic time series models. To describe this phenomenon we introduce a logistic smooth transition autoregressive model where the regime switches depend on the forecast of the time series of interest. This forecast can either be an exogenous expert forecast or an endogenous forecast generated by the model. Results of an application of the model to US inflation shows that (i) forecasts lead to regime changes and have an impact on the level of inflation; (ii) a relatively large forecast results in actions which in the end lower the inflation rate; (iii) a counterfactual scenario where forecasts during the oil crises in the 1970s are assumed to be correct leads to lower inflation than observed.
    Keywords: forecasting, inflation, nonlinear time series, regime switching
    Date: 2013–08–08
  5. By: Hoornweg, V.
    Abstract: __Abstract__ We use automated algorithms to update and evaluate ad hoc judgments that are made in applied econometrics. Such an application of automated algorithms robustifies empirical econometric analyses, it achieves lower and more consistent prediction errors, and it helps to prevent data snooping. Tools are introduced to evaluate the algorithm, to see how configurations are updated by the algorithm, to study how forecasting accuracy is affected by the choice of configurations, and to find out which configurations can safely be ignored in order to increase the speed of the algorithm. In our case study we develop an algorithm that updates ad hoc judgments that are made in Cápistran and Timmermann's (2009) attempt to beat the mean survey forecast. Many of these ad hoc judgments are often made in time series forecasting and have hitherto been overlooked. We show that our algorithm improves their models and at the same time we further robustify the stylized fact that the mean survey forecast is difficult to beat. JEL classificatie is trouwens C52, mocht je dat nodig hebben.
    Keywords: robust, ad hoc, automated, algorithm, update, combine, forecast
    JEL: C52
    Date: 2013–11–01
  6. By: Kim, T.Y.; Dekker, R.; Heij, C.
    Abstract: Efficiency of outbound warehouse operations depends on the management of demand forecasts and associated labor planning. A case study in consumer electronics shows that warehouse management systematically over-forecasts actual orders, by 3% on average and by 6-12% in busy periods (at the end of each month and also in the months September, October, and November). A time series model that corrects order forecasts for the biases in preceding weeks reduces the bias to less than 2%, both on average and also in busy periods. The arrangements with the labor provider imply potential benefits of intentional over-forecasting and the associated ample labor supply for the warehouse. As compared to under-forecasted days, labor productivity on over-forecasted days is higher by 12% for loading activities and by 4% for picking and total outbound activities. Similar productivity gains are found if unbiased forecasts are compared with the optimal bias obtained from non-linear models estimated from daily data on bias and labor efficiency. The positive effects of intentional over-forecasting on productivity are confirmed in a structural equations model. By following similar methodologies as described in this paper, warehouse managers can determine the amount of intentional forecast bias that works best for their situation. The information required for this kind of evidence-based labor management consists of historical data on order sizes, forecasts, and labor productivity, and the outcomes depend on the available hiring strategies and cost structures.
    Keywords: case study, decision support, forecasting, labor efficiency, time series, wharehouse planning
    Date: 2013–05–01
  7. By: Kole, H.J.W.G.; van Dijk, D.J.C.
    Abstract: The state of the equity market, often referred to as a bull or a bear market, is of key importance for financial decisions and economic analyses. Its latent nature has led to several methods to identify past and current states of the market and forecast future states. These methods encompass semi-parametric rule-based methods and parametric regime-switching models. We compare these methods by new statistical and economic measures that take into account the latent nature of the market state. The statistical measure is based directly on the predictions, while the economic mea- sure is based on the utility that results when a risk-averse agent uses the predictions in an investment decision. Our application of this framework to the S&P500 shows that rule-based methods are preferable for (in-sample) identification of the market state, but regime-switching models for (out-of-sample) forecasting. In-sample only the direction of the market matters, but for forecasting both means and volatilities of returns are important. Both the statistical and the economic measures indicate that these differences are significant.
    Keywords: economic comparison, forecast evaluation, regime switching, stock market
    JEL: C52 C53 G11 G17 G3 M00
    Date: 2013–10–14
  8. By: Tao Xiong; Yukun Bao; Zhongyi Hu
    Abstract: Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furthermore, this study proposes a firefly algorithm (FA)-based approach, built on the established MSVR, for determining the parameters of MSVR (abbreviated as FA-MSVR). Three globally traded broad market indices are used to compare the performance of the proposed FA-MSVR method with selected counterparts. The quantitative and comprehensive assessments are performed on the basis of statistical criteria, economic criteria, and computational cost. In terms of statistical criteria, we compare the out-of-sample forecasting using goodness-of-forecast measures and testing approaches. In terms of economic criteria, we assess the relative forecast performance with a simple trading strategy. The results obtained in this study indicate that the proposed FA-MSVR method is a promising alternative for forecasting interval-valued financial time series.
    Date: 2014–01
  9. By: Chang, C-L.; McAleer, M.J.
    Abstract: Experts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is made with publicly available bibliometric data, namely the Thomson Reuters ISI Web of Science citations database (hereafter ISI) for the Business - Finance (hereafter Finance) category. The paper analyses the leading international journals in Finance using expert scores and quantifiable Research Assessment Measures (RAMs), and highlights the similarities and differences in the expert scores and alternative RAMs, where the RAMs are based on alternative transformations of citations taken from the ISI database. Alternative RAMs may be calculated annually or updated daily to answer the perennial questions as to When, Where and How (frequently) published papers are cited (see Chang et al. (2011a, b, c)). The RAMs include the most widely used RAM, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, PI-BETA (Papers Ignored - By Even The Authors), 2-year Self-citation Threshold Approval Ratings (2Y-STAR), Historical Self-citation Threshold Approval Ratings (H-STAR), Impact Factor Inflation (IFI), and Cited Article Influence (CAI). As data are not available for 5YIF, Article Influence and CAI for 13 of the leading 34 journals considered, 10 RAMs are analysed for 21 highly-cited journals in Finance. The harmonic mean of the ranks of the 10 RAMs for the 34 highly-cited journals are also presented. It is shown that emphasizing the 2-year impact factor of a journal, which partly answers the question as to When published papers are cited, to the exclusion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal impact and influence relative to the Harmonic Mean rankings. A linear regression model is used to forecast expert scores on the basis of RAMs that capture journal impact, journal policy, the number of high quality papers, and quantitative information about a journal. The robustness of the rankings is also analysed.
    Keywords: C3PO, IFI, PI-BETA, RAMs, STAR, article influence, eigenfactor, expert scores, h-index, harmonic mean, impact factor, journal quality, robustness
    JEL: C81 C83
    Date: 2013–02–01
  10. By: Chang, C-L.; Allen, D.E.; McAleer, M.J.; Pérez-Amaral, T.
    Abstract: The papers in this special issue of Mathematics and Computers in Simulation are substantially revised versions of the papers that were presented at the 2011 Madrid International Conference on “Risk Modelling and Management” (RMM2011). The papers cover the following topics: currency hedging strategies using dynamic multivariate GARCH, risk management of risk under the Basel Accord: A Bayesian approach to forecasting value-at-risk of VIX futures, fast clustering of GARCH processes via Gaussian mixture models, GFC-robust risk management under the Basel Accord using extreme value methodologies, volatility spillovers from the Chinese stock market to economic neighbours, a detailed comparison of Value-at-Risk estimates, the dynamics of BRICS's country risk ratings and domestic stock markets, U.S. stock market and oil price, forecasting value-at-risk with a duration-based POT method, and extreme market risk and extreme value theory.
    Keywords: BRICS, Basel Accord, VIX futures, country risk ratings, currency hedging strategies, extreme market risks, extreme value methodologies, fast clustering, forecasting, mixture models, risk management, value-at-risk, volatility spillovers
    JEL: C14 C32 C53 G11 G32
    Date: 2013–06–01

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