nep-for New Economics Papers
on Forecasting
Issue of 2007‒02‒24
four papers chosen by
Rob J Hyndman
Monash University

  1. Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting By Matteo Manera; Chiara Longo; Anil Markandya; Elisa Scarpa
  2. Forecasting with Panel Data By Badi H. Baltagi
  3. Option-implied preferences adjustments, density forecasts, and the equity risk premium By Francisco Alonso; Roberto Blanco; Gonzalo Rubio
  4. Tourism in the Canary Islands: Forecasting Using Several Seasonal Time Series Models By Juncal Cuñado; Luis A. Gil-Alaña

  1. By: Matteo Manera (University of Milan Bicocca); Chiara Longo (Fondazione Eni Enrico Mattei); Anil Markandya (University of Bath and Fondazione Eni Enrico Mattei); Elisa Scarpa (Risk Management Department, Intesa-San Paolo)
    Abstract: The relevance of oil in the world economy explains why considerable effort has been devoted to the development of different types of econometric models for oil price forecasting. Several specifications have been proposed in the economic literature. Some are based on financial theory and concentrate on the relationship between spot and futures prices (“financial” models). Others assign a key role to variables explaining the characteristics of the physical oil market (“structural” models). The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static and dynamic forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature (“mixed” models). Our empirical findings can be summarized as follows. Financial models in levels do not produce satisfactory forecasts for the WTI spot price. The financial error correction model yields accurate in-sample forecasts. Real and strategic variables alone are insufficient to capture the oil spot price dynamics in the forecasting sample. Our proposed mixed models are statistically adequate and exhibit accurate forecasts. Different data frequencies seem to affect the forecasting ability of the models under analysis.
    Keywords: Oil Price, WTI Spot And Futures Prices, Forecasting, Econometric Models
    JEL: C52 C53 Q32 Q43
    Date: 2007–01
  2. By: Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020)
    Abstract: This paper gives a brief survey of forecastiang with panel data. Starting with a simple error component regression model and surveying best linear unbiased prediction under various assumptions of the disturbance term. This includes various ARMA models as well as spatial autoregressive models. The paper also surveys how these forecasts have been used in panel data applications, running horse races between heterogeneous and homogeneous panel data models using out of sample forecasts.
    Keywords: forecasting; BLUP; panel data; spatial dependence; serial correlation; heterogeneous panels.
    JEL: C33
  3. By: Francisco Alonso (Banco de España); Roberto Blanco (Banco de España); Gonzalo Rubio (Euskal Herriko Unibertsitatea; Universitat Pompeu Fabra)
    Abstract: The main objective of this paper is to analyse the value of information contained in prices of options on the IBEX 35 index at the Spanish Stock Exchange Market. The forward looking information is extracted using implied risk-neutral density functions estimated by a mixture of two lognormals and several alternative risk adjustments: the power, exponential and habit inspired based stochastic discount factors. Moreover, by allowing additional flexibility in the shape of the stochastic discount factor, two other ad hoc time varying risk aversion adjustments are also employed. Our results show that, between October 1996 and March 2000, we can reject the hypothesis that the risk neutral densities provide accurate predictions of the distributions of future realisations of the IBEX 35 index at four and eight week horizons. When forecasting through risk adjusted densities the performance of this period is statistically improved and we no longer reject that hypothesis. Somehow surprisingly, all risk adjusted densities generate similar forecasting statistics. Finally, from October 1996 to December 2004, the ex ante risk premium perceived by investors and that are embedded in option prices is between 12 and 18 percent higher than the premium required to compensate the same investors for the realised volatility in stock market returns.
    Keywords: risk-adjustments, option-implied densities, forecasting performance, equity-risk premium
    JEL: G10 G12
    Date: 2006–11
  4. By: Juncal Cuñado (Universidad de Navarra); Luis A. Gil-Alaña (Universidad de Navarra)
    Abstract: This paper deals with the analysis of the number of tourists travelling to the Canary Islands by means of using different seasonal statistical models. Deterministic and stochastic seasonality is considered. For the latter case, we employ seasonal unit roots and seasonally fractionally integrated models. As a final approach, we also employ a model with possibly different orders of integration at zero and the seasonal frequencies. All these models are compared in terms of their forecasting ability in an out-of-sample experiment. The results in the paper show that a simple deterministic model with seasonal dummy variables and AR(1) disturbances produce better results than other approaches based on seasonal fractional and integer differentiation over short horizons. However, increasing the time horizon, the results cannot distinguish between the model based on seasonal dummies and another using fractional integration at zero and the seasonal frequencies.

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