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Wyszukujesz frazę "predictive distribution" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Examination of Seasonal Volatility in HICP for Baltic Region Countries: Non-Parametric Test versus Forecasting Experiment
Autorzy:
Lenart, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/2076445.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
HICP
seasonal volatility
exponential smoothing
nowcasting
predictive distribution
logscore
Opis:
The aim of this paper is to examine the problem of existing seasonal volatility in total and disaggregated HICP for Baltic Region countries (Denmark, Estonia, Latvia, Finland, Germany, Lithuania, Poland and Sweden). Using nonparametric tests, we found that in the case of m-o-m prices, including fruit, vegetables, and total HICP, the homogeneity of variance during seasons is rejected. Based on these findings, we propose an exponential smoothing model with periodic variance of error terms that capture the repetitive seasonal variation (in conditional or unconditional second moments). In a pseudo-real data experiment, the short-term forecasts (nowcasting) for the considered components of inflation were determined using different specifications of considered models. The forecasting performance of the models was measured using one of the scoring rules for probabilistic forecasts called logarithmic score. We found instead that while the periodic phenomenon in variance was statistically significant, the models with a periodic phenomenon in variance of error terms do not significantly improve forecasting performance in disaggregated cases and in the case of total HICP. The simpler models with constant variance of error term have comparative forecasting (nowcasting) performance over the alternative model
Źródło:
Central European Journal of Economic Modelling and Econometrics; 2017, 1; 29-67
2080-0886
2080-119X
Pojawia się w:
Central European Journal of Economic Modelling and Econometrics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bayesian Inference for State Space Model with Panel Data
Autorzy:
Pandey, Ranjita
Chaturvedi, Anoop
Powiązania:
https://bibliotekanauki.pl/articles/466044.pdf
Data publikacji:
2016
Wydawca:
Główny Urząd Statystyczny
Tematy:
Bayesian analysis
Gibbs sampler
conditional posterior densities
predictive distribution
Opis:
The present work explores panel data set-up in a Bayesian state space model. The conditional posterior densities of parameters are utilized to determine the marginal posterior densities using the Gibbs sampler. An efficient one step ahead predictive density mechanism is developed to further the state of art in prediction-based decision making.
Źródło:
Statistics in Transition new series; 2016, 17, 2; 211-220
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combining predictive distributions of electricity prices : does minimizing the CRPS lead to optimal decisions in day-ahead bidding?
Autorzy:
Nitka, Weronika
Weron, Rafał
Powiązania:
https://bibliotekanauki.pl/articles/27315321.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
decision support
day-ahead electricity bidding
predictive distribution
combining forecast
CRPS learning
Opis:
Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no model is perfect and averaging generally improves forecasting performance. In this article, we address the question of whether using CRPS learning, a novel weighting technique minimizing the continuous ranked probability score (CRPS), leads to optimal decisions in day-ahead bidding. To this end, we conduct an empirical study using hourly day-ahead electricity prices from the German EPEX market. We find that increasing the diversity of an ensemble can have a positive impact on accuracy. At the same time, the higher computational cost of using CRPS learning compared to an equal-weighted aggregation of distributions is not offset by higher profits, despite significantly more accurate predictions.
Źródło:
Operations Research and Decisions; 2023, 33, 3; 105--118
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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