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on Forecasting |
| By: | Rick Steinert; Florian Ziel |
| Abstract: | Due to the liberalization of markets, the change in the energy mix and the surrounding energy laws, electricity research is a dynamically altering field with steadily changing challenges. One challenge especially for investment decisions is to provide reliable short to mid-term forecasts despite high variation in the time series of electricity prices. This paper tackles this issue in a promising and novel approach. By combining the precision of econometric autoregressive models in the short-run with the expectations of market participants reflected in future prices for the short- and mid-run we show that the forecasting performance can be vastly increased while maintaining hourly precision. We investigate the day-ahead electricity price of the EPEX Spot for Germany and Austria and setup a model which incorporates the Phelix future of the EEX for Germany and Austria. The model can be considered as an AR24-X model with one distinct model for each hour of the day. We are able to show that future data contains relevant price information for future time periods of the day-ahead electricity price. We show that relying only on deterministic external regressors can provide stability for forecast horizons of multiple weeks. By implementing a fast and efficient lasso estimation approach we demonstrate that our model can outperform several other models in the literature. |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1801.10583&r=for |
| By: | Bastien Alonzo (IPSL; LMD; CNRS; Ecole Polytechnique; Université de Paris-Saclay; Laboratoire de Probabilités et Modéles Aléatoires, Université Paris Diderot-Paris 7); Philippe Drobinski (IPSL; LMD; CNRS; Ecole Polytechnique; Université de Paris-Saclay); Riwal Plougonven (IPSL; LMD; CNRS; Ecole Polytechnique; Université de Paris-Saclay); Peter Tankov (CREST; ENSAE ParisTech) |
| Abstract: | We build and evaluate a probabilistic model designed for forecasting the distribution of the daily mean wind speed at the seasonal timescale in France. On such long-term timescales, the variability of the surface wind speed is strongly in uenced by the atmosphere large-scale situation. Our aim is to predict the daily mean wind speed distribution at a speci c location using the information on the atmosphere large-scale situation, summarized by an index. To this end, we estimate, over 20 years of daily data, the conditional probability density function of the wind speed given the index. We next use the ECMWF seasonal forecast ensemble to predict the atmosphere large-scale situation and the index at the seasonal timescale. We show that the model is sharper than the climatology at the monthly horizon, even if it displays a strong loss of precision after 15 days. Using a statistical postprocessing method to recalibrate the ensemble forecast leads to further improvement of our probabilistic forecast, which then remains sharper than the climatology at the seasonal horizon. |
| Keywords: | Wind energy, Wind speed forecasting, Seasonal forecasting, Probabilistic forecasting, Ensemble forecasts, Ensemble model output statistics |
| Date: | 2017–10–11 |
| URL: | http://d.repec.org/n?u=RePEc:crs:wpaper:2017-88&r=for |
| By: | Thomas Lustenberger; Enzo Rossi (University of Basel) |
| Abstract: | In a large sample of countries across different geographic regions andover a long period of time, we find limited country- and variable-specific effectsof central bank transparency on forecast accuracy and their dispersionamong a large set of professional forecasts of financial and macroeconomicvariables. More communication even increases forecast errors and dispersion. |
| Keywords: | Central bank transparency, central bank communication, central bank independence, inflation targeting, forward guidance, macroeconomic forecasts, financial forecasts, panel data models with truncated data |
| JEL: | C23 C53 E37 E58 D8 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:bsl:wpaper:2018/06&r=for |
| By: | Frédérique Bec (Thema; University of Cergy-Pontoise;CREST); Raouf Boucekkine (Aix-Marseille University; CNRS; EHESS; Centrale Marseille; AMSE and IMéra); Caroline Jardet (Banque de France, DGEI-DCPM) |
| 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 time- and 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 co-existence 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: | 2017–11–07 |
| URL: | http://d.repec.org/n?u=RePEc:crs:wpaper:2017-17&r=for |
| By: | Koop, G; Korobilis, D |
| Abstract: | This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time-varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coeffcients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation-free algorithm that relies on an analytical approximation of the posterior distribution. We use our methods to forecast inflation rates in the eurozone and show that forecasts from our flexible specification are superior to alternative methods for large vector autoregressions. |
| Keywords: | Panel VAR, inflation forecasting, Bayesian, time-varying parameter model |
| Date: | 2018–01–31 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:21329&r=for |
| By: | Maheu, John M; Song, Yong; Yang, Qiao |
| Abstract: | This paper shows that oil shocks primarily impact economic growth through the conditional variance of growth. We move beyond the literature that focuses on conditional mean point forecasts and compare models based on density forecasts. Over a range of dynamic models, oil shock measures and data we find a robust link between oil shocks and the volatility of economic growth. A new measure of oil shocks is developed and shown to be superior to existing measures and indicates that the conditional variance of growth increases in response to an indicator of local maximum oil price exceedance. The empirical results uncover a large pronounced asymmetric response of growth volatility to oil price changes. Uncertainty about future growth is considerably lower compared to a benchmark AR(1) model when no oil shocks are present. |
| Keywords: | Bayes factors, predictive likelihoods, nonlinear dynamics, density forecast |
| JEL: | C11 C32 C53 Q43 |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:83999&r=for |
| By: | Håvard Hungnes (Statistics Norway) |
| Abstract: | The paper suggests two encompassing tests for evaluating multi-step system forecasts invariant to linear transformations. An invariant measure for forecast accuracy is necessary as the conclusions otherwise can depend on how the forecasts are reported (e.g., as in level or growth rates). Therefore, a measure based on the prediction likelihood of the forecast for all variables at all horizons is used. Both tests are based on a generalization of the encompassing test for univariate forecasts where potential heteroscedasticity and autocorrelation in the forecasts are considered. The tests are used in evaluating quarterly multi-step system forecasts made by Statistics Norway. |
| Keywords: | Macroeconomic forecasts; Econometric models; Forecast performance; Forecast evaluation; Forecast comparison |
| JEL: | C32 C53 |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:ssb:dispap:871&r=for |
| By: | Berg-Andersson, Birgitta; Kaitila, Ville; Kaseva, Hannu; Kotilainen, Markku; Lehmus, Markku |
| Abstract: | In forecasts for years 2014–2015 the Finnish GDP growth was overestimated, and in forecasts for years 2016–2017 it was underestimated. From 2013 to mid-2015 the forecasters anticipated that the recovery in the world economy, and correspondingly in the Finnish economy, would have been stronger than what was realized. In winter 2015–2016 there was a stock exchange crash in China that created uncertainty in the whole world. Forecasts concerning the world economy were generally lowered. This was one reason for the overly pessimism in the early forecasts concerning the Finnish GDP growth for 2016 and 2017. In Finland concern caused also the past unsatisfactory export performance and the deterioration of the cost competitiveness. Additionally, it was difficult to estimate, how the economy will recover from the end of mobile phone production in Finland. In forecasts for the development of the private consumption, the main difficulty was forecasting the steep drop in the savings rate to a clear negative territory. |
| Date: | 2018–02–07 |
| URL: | http://d.repec.org/n?u=RePEc:rif:briefs:63&r=for |
| By: | Maheu, John M; Yang, Qiao; Song, Yong |
| Abstract: | This paper shows that oil shocks primarily impact economic growth through the conditional variance of growth. We move beyond the literature that focuses on conditional mean point forecasts and compare models based on density forecasts. Over a range of dynamic models, oil shock measures and data we find a robust link between oil shocks and the volatility of economic growth. A new measure of oil shocks is developed and shown to be superior to existing measures and indicates that the conditional variance of growth increases in response to an indicator of local maximum oil price exceedance. The empirical results uncover a large pronounced asymmetric response of growth volatility to oil price changes. |
| Keywords: | Bayes factors, predictive likelihoods, nonlinear dynamics, density forecast |
| JEL: | C11 C32 C53 Q43 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:83779&r=for |
| By: | Gerlach, Richard; Naimoli, Antonio; Storti, Giuseppe |
| Abstract: | This paper proposes generalisations of the Realized GARCH model by Hansen et al. (2012), in three different directions. First, heteroskedasticity in the noise term in the measurement equation is allowed, since this is generally assumed to be time-varying as a function of an estimator of the Integrated Quarticity for intra-daily returns. Second, in order to account for attenuation bias effects, the volatility dynamics are allowed to depend on the accuracy of the realized measure. This is achieved by letting the response coefficient of the lagged realized measure depend on the time-varying variance of the volatility measurement error, thus giving more weight to lagged volatilities when they are more accurately measured. Finally, a further extension is proposed by introducing an additional explanatory variable into the measurement equation, aiming to quantify the bias due to effect of jumps and measurement errors. |
| Keywords: | Realized Volatility, Realized GARCH, Measurement Error, Realized Quarticity |
| JEL: | C22 C53 C58 |
| Date: | 2018–01–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:83893&r=for |