|
on Forecasting |
| By: | Barth, Mary E. (Stanford University); Clinch, Greg (University of Melbourne); Ma, Paul (University of Minnesota) |
| Abstract: | We address whether mandatory forecasts of earnings announcement dates are informative and what are the informational tradeoffs between mandatory and voluntary forecasts. We find China mandatory forecasts predict actual earnings announcement dates and yet-to-be-announced firm performance, and the market reacts to the initial and revised forecasts accordingly. Regarding informational tradeoffs we find the following. China mandatory forecasts are informative, even by firms less likely to issue a voluntary forecast; this information is unavailable in a voluntary regime. The act of US voluntary forecasting and its timing reveal information incremental to the forecasted announcement date, which is unavailable in a mandatory regime. Perhaps surprisingly, US voluntary and China mandatory initial forecasts convey a similar amount of earnings news, which is noteworthy because the China forecasts are issued substantially earlier and suggests the amount of information in the act and timing of voluntary forecasts is small. |
| JEL: | D84 G14 M48 |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:stabus:3661&r=for |
| By: | Rafal Weron; Florian Ziel |
| Abstract: | Electricity price forecasting (EPF) is a branch of energy forecasting on the interface between econometrics/statistics and engineering, which focuses on predicting the spot and forward prices in wholesale electricity markets. Its beginnings can be traced back to the early 1990s, when power sector deregulation led to the introduction of competitive markets in the UK and Scandinavia. The changes quickly spread throughout Europe and North America, and nowadays - in many countries worldwide - electricity is traded under market rules using spot and derivative contracts. Over the last 25 years, a variety of methods and ideas have been tried for EPF, with varying degrees of success. In this chapter we first briefly discuss the forecasting horizons and the types of forecasts, then review the forecasting tools and the evaluation techniques used in the EPF literature. |
| Keywords: | Electricity price forecasting; Probabilistic forecast; Ensemble forecast; Day-ahead market; Intraday market; Regression; Computational intelligence; Reduced-form model; Multi-agent simulation; Model evaluation; Predictive ability test |
| JEL: | C22 C32 C45 C51 C53 C70 L11 Q41 Q47 |
| Date: | 2018–09–09 |
| URL: | http://d.repec.org/n?u=RePEc:wuu:wpaper:hsc1808&r=for |
| By: | Knut Are Aastveit (Norges Bank); James Mitchell (Warwick Business School); Francesco Ravazzolo (Free University of Bozen/Bolzano); Herman van Dijk (Erasmus University, Noges Bank) |
| Abstract: | Increasingly, professional forecasters and academic researchers present model-based and subjective or judgment-based forecasts in economics which are accompanied by some measure of uncertainty. In its most complete form this measure is a probability density function for future values of the variables of interest. At the same time combinations of forecast densities are being used in order to integrate information coming from several sources like experts, models and large micro-data sets. Given this increased relevance of forecast density combinations, the genesis and evolution of this approach, both inside and outside economics, is explored. A fundamental density combination equation is specified which shows that various frequentist as well as Bayesian approaches give different specific contents to this density. In its most simplistic case, it is a restricted finite mixture, giving fixed equal weights to the various individual densities. The specification of the fundamental density combination is made more flexible in recent literature. It has evolved from using simple average weights to optimized weights and then to `richer' procedures that allow for time-variation, learning features and model incompleteness. The recent history and evolution of forecast density combination methods, together with their potential and benefits, are illustrated in a policy making environment of central banks. |
| Keywords: | Forecasting; Model Uncertainty; Density Combinations |
| JEL: | C10 C11 |
| Date: | 2018–09–02 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20180069&r=for |
| By: | Giacoletti, Marco (U of Southern California); Laursen, Kristoffer T. (AQR Capital Management, LLC); Singleton, Kenneth J. (Stanford U) |
| Abstract: | We study the evolution of risk premiums on US Treasury bonds from the perspective of a real-time Bayesian learner RA who updates her beliefs using a dynamic term structure model. Learning about the historical dynamics of yields led to substantial variation in RA's subjective risk premiums. Moreover, she gained substantial forecasting power by conditioning her learning on measures of disagreement among professional forecasters about future yields. This gain was distinct from the (much weaker) forecasting power of macroeconomic information. RA's views about the pricing distribution of yields remained nearly constant over time. Her learning rule outperformed consensus forecasts of market professionals, particularly following U.S. recessions. |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:stabus:3670&r=for |