19,402 research outputs found
Evaluation of ECMWF medium-range ensemble forecasts of precipitation for river basins
Providing probabilistic forecasts using Ensemble Prediction Systems has become increasingly popular in both the meteorological and hydrological communities. Compared to conventional deterministic forecasts, probabilistic forecasts may provide more reliable forecasts of a few hours to a number of days ahead, and hence are regarded as better tools for taking uncertainties into consideration and hedging against weather risks. It is essential to evaluate performance of raw ensemble forecasts and their potential values in forecasting extreme hydro-meteorological events. This study evaluates ECMWF's medium-range ensemble forecasts of precipitation over the period 1 January 2008 to 30 September 2012 on a selected midlatitude large-scale river basin, the Huai river basin (ca. 270 000 km2) in central-east China. The evaluation unit is sub-basin in order to consider forecast performance in a hydrologically relevant way. The study finds that forecast performance varies with sub-basin properties, between flooding and non-flooding seasons, and with the forecast properties of aggregated time steps and lead times. Although the study does not evaluate any hydrological applications of the ensemble precipitation forecasts, its results have direct implications in hydrological forecasts should these ensemble precipitation forecasts be employed in hydrology
Evaluation of ECMWF medium-range ensemble forecasts of precipitation for river basins
Providing probabilistic forecasts using Ensemble Prediction Systems has become increasingly popular in both the meteorological and hydrological communities. Compared to conventional deterministic forecasts, probabilistic forecasts may provide more reliable forecasts of a few hours to a number of days ahead, and hence are regarded as better tools for taking uncertainties into consideration and hedging against weather risks. It is essential to evaluate performance of raw ensemble forecasts and their potential values in forecasting extreme hydro-meteorological events. This study evaluates ECMWF's medium-range ensemble forecasts of precipitation over the period 1 January 2008 to 30 September 2012 on a selected midlatitude large-scale river basin, the Huai river basin (ca. 270 000 km2) in central-east China. The evaluation unit is sub-basin in order to consider forecast performance in a hydrologically relevant way. The study finds that forecast performance varies with sub-basin properties, between flooding and non-flooding seasons, and with the forecast properties of aggregated time steps and lead times. Although the study does not evaluate any hydrological applications of the ensemble precipitation forecasts, its results have direct implications in hydrological forecasts should these ensemble precipitation forecasts be employed in hydrology
Methods Available to Monetary Policy Makers to Deal with Uncertainty
Three sources â research on monetary policy under uncertainty, the managerial literature, and the real-life strategies of five inflation targeters â have been used to survey methods that are available to monetary policy makers to deal with uncertainty. The methods have been compared within a framework that is based on a decision matrix. The comparative framework has been designed in order to encompass different representations of uncertainty employed by various central banks. The results of comparative analysis suggest that central banks use models, intuition, judgement as well as traditional managerial methods to deal with uncertainty. This finding helps understanding why economic research cannot fully explain differences between monetary policy actions and outcomes of model simulations. The results of the comparative analysis also suggest that central banks have not so far fully utilised the whole spectrum of methods available to them. Economic research, other banksâ strategies as well as decision analysis may be interesting sources of inspiration when designing the decision-making process. It is emphasised that central banks introducing inflation targeting should pay equal attention to both building their forecasting models as well as selecting methods to deal with uncertainty. In the case of emerging economies where uncertainty can be much higher than in advanced economies, neglecting uncertainty may increase probability of policy errors significantly.Inflation targeting Uncertainty Decision matrix Survey of methods
What is the best risk measure in practice? A comparison of standard measures
Expected Shortfall (ES) has been widely accepted as a risk measure that is
conceptually superior to Value-at-Risk (VaR). At the same time, however, it has
been criticised for issues relating to backtesting. In particular, ES has been
found not to be elicitable which means that backtesting for ES is less
straightforward than, e.g., backtesting for VaR. Expectiles have been suggested
as potentially better alternatives to both ES and VaR. In this paper, we
revisit commonly accepted desirable properties of risk measures like coherence,
comonotonic additivity, robustness and elicitability. We check VaR, ES and
Expectiles with regard to whether or not they enjoy these properties, with
particular emphasis on Expectiles. We also consider their impact on capital
allocation, an important issue in risk management. We find that, despite the
caveats that apply to the estimation and backtesting of ES, it can be
considered a good risk measure. As a consequence, there is no sufficient
evidence to justify an all-inclusive replacement of ES by Expectiles in
applications. For backtesting ES, we propose an empirical approach that
consists in replacing ES by a set of four quantiles, which should allow to make
use of backtesting methods for VaR.
Keywords: Backtesting; capital allocation; coherence; diversification;
elicitability; expected shortfall; expectile; forecasts; probability integral
transform (PIT); risk measure; risk management; robustness; value-at-riskComment: 27 pages, 1 tabl
Dynamic Pooling for the Combination of Forecasts Generated Using Multi Level Learning
In this paper we provide experimental results and
extensions to our previous theoretical findings concerning the
combination of forecasts that have been diversified by three
different methods: with parameters learned at different data
aggregation levels, by thick modeling and by the use of different
forecasting methods. An approach of error variance based
pooling as proposed by Aiolfi and Timmermann has been compared
with flat combinations as well as an alternative pooling
approach in which we consider information about the used
diversification. An advantage of our approach is that it leads to
the generation of novel multi step multi level forecast generation
structures that carry out the combination in different steps of
pooling corresponding to the different types of diversification.
We describe different evolutionary approaches in order to
evolve the order of pooling of the diversification dimensions.
Extensions of such evolutions allow the generation of more
flexible multi level multi step combination structures containing
better adaptive capabilities. We could prove a significant error
reduction comparing results of our generated combination
structures with results generated with the algorithm of Aiolfi
and Timmermann as well as with flat combination for the
application of Revenue Management seasonal forecasting
Using Non-Parametric Tests to Evaluate Traffic Forecasting Performance.
This paper proposes the use of a number of nonparametric comparison methods for evaluating traffic flow forecasting techniques. The advantage to these methods is that they are free of any distributional assumptions and can be legitimately used on small datasets. To demonstrate the applicability of these tests, a number of models for the forecasting of traffic flows are developed. The one-step-ahead forecasts produced are then assessed using nonparametric methods. Consideration is given as to whether a method is universally good or good at reproducing a particular aspect of the original series. That choice will be dictated, to a degree, by the userâs purpose for assessing traffic flow
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