nep-for New Economics Papers
on Forecasting
Issue of 2017‒12‒11
four papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Macedonian Inflation: Evaluation of different models for short-term forecasting By Magdalena Petrovska; Gani Ramadani; Nikola Naumovski; Biljana Jovanovic
  2. Forecasting GDP with energy series: ADL-MIDAS vs. Linear Time Series Models By Afees A. Salisu; Ahaemefula Ephraim Ogbonna
  3. Why are inflation forecasts sticky? Theory and application to France and Germany By F. Bec; R. Boucekkine; C. Jardet
  4. On the Tail Risk Premium in the Oil Market By Reinhard Ellwanger

  1. By: Magdalena Petrovska (National Bank of the Republic of Macedonia); Gani Ramadani (National Bank of the Republic of Macedonia); Nikola Naumovski (National Bank of the Republic of Macedonia); Biljana Jovanovic (National Bank of the Republic of Macedonia)
    Abstract: The primary goal of this paper is to describe several models that are currently used at the National Bank of the Republic of Macedonia (NBRM) for short-term forecasting of inflation - Autoregressive integrated moving average models (aggregated and disaggregated approach), three equation structural model and a dynamic factor model. Additionally, we evaluate models’ out-of-sample forecasting performance for the period 2012 q3 to 2016 q2 by using a number of forecast evaluation criteria such as the Root Mean Squared Error, the Mean Absolute Error, the Mean Absolute Percentage Error and the Theil’s U Statistics. Additionally, we constructed several composite forecasts in order to test whether a combination forecast is superior to individual models’ forecasts. Our results point to three important conclusions. First, the forecasting accuracy of the models is highest when they are used for forecasting one quarter ahead i.e. the errors increase as the forecasting horizon increases. Second, the disaggregated ARIMA model has the smallest forecasting errors. Third, majority of the forecast evaluation criteria suggest that composite forecasts are superior in comparison to the individual models.
    Keywords: Inflation, forecasting, forecast evaluation, composite forecast
    JEL: C52 C53 E37
    Date: 2017
  2. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan); Ahaemefula Ephraim Ogbonna (Centre for Econometric and Allied Research, University of Ibadan)
    Abstract: In this paper, we offer the following contributions to the extant literature on the energy-growth nexus. First, we test the predictability of energy series in the predictive growth model using autoregressive distributed lag mixed data sample (ADL-MIDAS) approach. Second, we compare the in-sample and out-of-sample forecast performance of the ADL-MIDAS model with the linear time series models involving the first order autoregressive [AR(1)] model and the autoregressive distributed lag (ARDL) model. Third, we consider an array of energy proxies ranging from aggregate data to sectoral data of energy consumption (residential, commercial, industrial and transportation) and those defined by energy sources (petroleum, natural gas, coal, electricity, nuclear electricity and renewable energy). Fourth, we test whether accounting for asymmetries matters in the ADL-MIDAS regression model for the energy-growth nexus. The results support the significant predictability of energy for growth regardless of the measures of energy. In addition, the in-sample and out-of-sample forecast results overwhelmingly favour the ADL-MIDAS over the conventional linear time series models including the restrictive AR model. Thus, allowing for high frequency data for energy in the low frequency growth model will enhance the forecast accuracy of the model. However, we find that accounting for asymmetries may not improve the forecast accuracy of the ADL-MIDAS model in the energy-growth nexus since forecasts of the positive and negative asymmetric models do not differ significantly.
    Keywords: Energy consumption; Growth, ADL-MIDAS; Linear time series models; Forecast evaluation
    JEL: C12 C22 Q42 Q43 Q47
    Date: 2017–11
  3. By: F. Bec; R. Boucekkine; C. Jardet
    Abstract: This paper proposes a theoretical model of forecasts formation which implies that in presence of information observation and forecasts communication costs, rational professional forecasters might find it optimal not to revise their forecasts continuously, or at any time. The threshold timeand state-dependence of the observation reviews and forecasts revisions implied by this model are then tested using inflation forecast updates of professional forecasters from recent Consensus Economics panel data for France and Germany. Our empirical results support the presence of both kinds of dependence, as well as their threshold-type shape. They also imply an upper bound of the optimal time between two information observations of about six months and the coexistence of both types of costs, the observation cost being about 1.5 times larger than the communication cost.
    Keywords: Forecast revision, binary choice models, information and communication costs.
    JEL: C23 D8 E31
    Date: 2017
  4. By: Reinhard Ellwanger
    Abstract: This paper shows that changes in market participants’ fear of rare events implied by crude oil options contribute to oil price volatility and oil return predictability. Using 25 years of historical data, we document economically large tail risk premia that vary substantially over time and significantly forecast crude oil futures and spot returns. Oil futures prices increase (decrease) in the presence of upside (downside) fears in order to allow for smaller (larger) returns thereafter. This increase (decrease) is amplified for the spot price because of time varying-benefits from holding physical oil inventories that work in the same direction. We also provide support for view that that time variation in the relative importance of oil demand and supply shocks is an important determinant of oil price fluctuations and their interaction with aggregate outcomes. However, the option-implied tail risk premia are not spanned by traditional macroeconomic and oil market uncertainty measures, suggesting that time-varying oil price fears are an additional source of oil price volatility and predictability.
    Keywords: Asset Pricing, Econometric and statistical methods, Financial markets
    JEL: C53 C58 D84 E44 G12 G13 Q43
    Date: 2017

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