nep-for New Economics Papers
on Forecasting
Issue of 2016‒07‒02
six papers chosen by
Rob J Hyndman
Monash University

  1. Estimating and forecasting generalized fractional Long memory stochastic volatility models By Shelton Peiris; Manabu Asai; Michael McAleer
  2. Forecasting inbound tourists in Cambodia By Tanaka, Kiyoyasu
  3. Validation of reference forecasts for passenger traffic By Andersson, Matts; Brundell-Freij, Karin; Eliasson, Jonas
  4. Methods of Long-term Forecasting: Comparative Analysis and Foreign Experience of Applying By Bodrova, Vera; Gvozdeva, Margarita; Kazakova, Maria
  5. Investments and uncertainty revisited: The case of the US economy By Degiannakis, Stavros; Filis, George; Palaiodimos, George
  6. News versus Sentiment : Predicting Stock Returns from News Stories By Heston, Steven L.; Sinha, Nitish R.

  1. By: Shelton Peiris (School of Mathematics and Statistics University of Sydney, Australia.); Manabu Asai (Faculty of Economics Soka University, Japan.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain.)
    Abstract: In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.
    Keywords: Stochastic volatility, GARCH models, Gegenbauer Polynomial, Long Memory, Spectral Likelihood, Estimation, Forecasting.
    JEL: C18 C21 C58
    Date: 2016–06
  2. By: Tanaka, Kiyoyasu
    Abstract: Forecasting tourism demand is crucial for management decisions in the tourism sector. Estimating a vector autoregressive (VAR) model for monthly visitor arrivals disaggregated by three entry points in Cambodia for the years 2006–2015, I forecast the number of arrivals for years 2016 and 2017. The results show that the VAR model fits well with the data on visitor arrivals for each entry point. Ex post forecasting shows that the forecasts closely match the observed data for visitor arrivals, thereby supporting the forecasting accuracy of the VAR model. Visitor arrivals to Siem Reap and Phnom Penh airports are forecast to increase steadily in future periods, with varying fluctuations across months and origin countries of foreign tourists.
    Keywords: Tourism, Econometric model, Tourism demand, Visitor arrivals, Forecasting, VAR, Cambodia
    JEL: C53 L83 Z32
    Date: 2016–06
  3. By: Andersson, Matts (WSP); Brundell-Freij, Karin (WSP); Eliasson, Jonas (KTH)
    Abstract: We have compared Swedish national forecasts for passenger traffic produced from 1975 to 2009 with the actual outcomes, and we found substantial differences between forecasts of passenger kilometers by mode and actual outcomes. In forecasts produced since the early 1990s, road and air traffic growth rates have generally been overpredicted, aggregate railway growth has been fairly accurate, commercial long-distance railway growth has been overpredicted, and the growth of subsidized intra-regional railway travel has been underpredicted following vast unanticipated supply increases. Focusing on car traffic forecasts, we show that a very large share of forecast errors can be explained by input variables turning out to be different than what was assumed in the forecasts. Even the original forecasts are much closer to actual outcomes than simple trendlines would have been, and once the input assumptions are corrected, the forecasts vastly outperform simple trendlines. The potential problems of using cross-sectional models for forecasting intertemporal changes thus seem to be limited. This tentative conclusion is also supported by the finding that elasticities from the cross-sectional models are consistent with those from a time-series model.
    Keywords: Demand modeling; Forecast; Transport; Accuracy; Validation
    JEL: R41 R42
    Date: 2016–06–07
  4. By: Bodrova, Vera (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Gvozdeva, Margarita (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Kazakova, Maria (Russian Presidential Academy of National Economy and Public Administration (RANEPA); Gaidar Institute for Economic Policy)
    Abstract: This paper examines the existing methods of long-term forecasting of macroeconomic indicators and analyzes the concept and theoretical basis of long-term fiscal sustainability of the country. In particular, we show that the planning and forecasting cover all aspects of socio-economic systems throughout the world's history. It is also noted that the analysis of current indicators of fiscal sustainability, such as budget deficits and public debt, are not enough for a full assessment of sustainability, since such figures do not take into account the implicit liabilities of the budget that may arise in the future. This requires a long-term forecast of socio-economic development of the country, based on internationally accepted methods of forecasting.
    Keywords: forecasting, prediction, socio-economic development, fiscal sustainability, macroeconomics
    Date: 2015–03–12
  5. By: Degiannakis, Stavros; Filis, George; Palaiodimos, George
    Abstract: This paper examines the relationship between investments and uncertainty for the US economy, as the latter is approximated by consumer sentiment, purchasing managers’ prospects and economic policy uncertainty. Contrary to the existing literature, we provide evidence that this relationship is time-varying. The time variation is attributed to the observed temporal replacement effect between private and public investments. Furthermore, we show that there are two distinct correlation regimes in this relationship and unless we concentrate on the two regimes, we cannot fully unravel the real link between uncertainty and investments. Finally, we examine whether the use of two correlation regimes provides better forecasts of investments compared to the use of the uncertainty indices alone. The forecasting exercise reveals that the use of correlation regimes provides statistically superior out-of-sample forecasts.
    Keywords: Uncertainty, public investments, private investments, gross fixed capital formation, dynamic correlation, forecast.
    JEL: C32 C51 C53 E22 H50
    Date: 2015–07–01
  6. By: Heston, Steven L.; Sinha, Nitish R.
    Abstract: This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news, however, predicts stock returns for one quarter. Positive news stories increase stock returns quickly, but negative stories have a long delayed reaction. Much of the delayed response to news occurs around the subsequent earnings announcement.
    Keywords: News ; Text Analysis
    JEL: G12 G14
    Date: 2016–06

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