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101.
《International Journal of Forecasting》2020,36(2):515-530
We develop a method for forecasting the distribution of the daily surface wind speed at timescales from 15-days to 3-months in France. On such long-term timescales, ensemble predictions of the surface wind speed have poor performance, however, the wind speed distribution may be related to the large-scale circulation of the atmosphere, for which the ensemble forecasts have better skill. The information from the large-scale circulation, represented by the 500 hPa geopotential height, is summarized into a single index by first running a PCA and then a polynomial regression. We estimate, over 20 years of daily data, the conditional probability density of the wind speed at a specific location given the index. We then use the ECMWF seasonal forecast ensemble to predict the index for horizons from 15-days to 3-months. These predictions are plugged into the conditional density to obtain a distributional forecast of surface wind. These probabilistic forecasts remain sharper than the climatology up to 1-month forecast horizon. Using a statistical postprocessing method to recalibrate the ensemble leads to further improvement of our probabilistic forecast, which then remains calibrated and sharper than the climatology up to 3-months horizon, particularly in the north of France in winter and fall. 相似文献
102.
103.
《International Journal of Forecasting》2020,36(4):1211-1227
In this paper, we focus on forecasting methods that use heterogeneous panels in the presence of cross-sectional dependence in terms of both spatial error dependence and common factors. We propose two main approaches to estimating the factor structure: a residuals-based approach, and an approach that uses a panel of auxiliary variables to extract the factors. Small sample properties of the proposed methods are investigated through Monte Carlo simulations and applied to predict house price inflation in OECD countries. 相似文献
104.
《International Journal of Forecasting》2020,36(2):684-694
This paper introduces a combination of asymmetry and extreme volatility effects in order to build superior extensions of the GARCH-MIDAS model for modeling and forecasting the stock volatility. Our in-sample results clearly verify that extreme shocks have a significant impact on the stock volatility and that the volatility can be influenced more by the asymmetry effect than by the extreme volatility effect in both the long and short term. Out-of-sample results with several robustness checks demonstrate that our proposed models can achieve better performances in forecasting the volatility. Furthermore, the improvement in predictive ability is attributed more strongly to the introduction of asymmetry and extreme volatility effects for the short-term volatility component. 相似文献
105.
《International Journal of Forecasting》2020,36(4):1517-1530
This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature. 相似文献
106.
《International Journal of Forecasting》2023,39(2):772-790
The literature suggests that the dispersion of agents’ forecasts of an event flows from heterogeneity of beliefs and models. Using a data set of fixed event point forecasts of UK GDP growth by a panel of independent forecasters published by HM Treasury, we investigate three questions concerning this dispersion: (a) Are agent’s beliefs randomly distributed or do agents fall into groups with similar beliefs? (b) as agents revise their forecasts, what roles are played by their previous and consensus forecasts? and (c) is an agent’s private information of persistent value? We find that agents fall into four clusters, a large majority, a few pessimists, and two idiosyncratic agents. Our proposed model of forecast revisions shows agents are influenced positively by a change in the consensus forecast and negatively influenced by the previous distance of their forecast from the consensus. We show that the forecasts of a minority of agents significantly lead the consensus. 相似文献
107.
《International Journal of Forecasting》2022,38(4):1400-1404
This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions. 相似文献
108.
《International Journal of Forecasting》2019,35(4):1460-1468
Team QUINKAN competed in the GEFCom2017 final match of hierarchical probabilistic load forecasting by adopting the quantile regression method using the R package quantreg. The weather stations were clustered into 11 groups, from which an optimal one was chosen for each load meter using the boosting method. The load meter records were cleaned and/or supplemented by various methods in order to secure robust quantile predictions. The variation in the regression formulas was kept as small as possible by introducing measures for suppressing prediction instability, although special formulas were employed for loading meters that were of an industrial nature. Several procedures were applied to help improve the accuracy, such as the smoothing of season transitions, coarse graining of the relative humidity, the use of load-oriented day-type definition, the averaging of weather data, and outlier removal. 相似文献
109.
尝试运用多元线性回归模型对河北省物流需求进行预测分析.在借鉴前人研究成果的基础上,选取研究指标,并且根据统计对数据的严格要求,选取了1990-2009年河北省统计年鉴上的相关指标作为数据来源,并对数据进行了逐步回归,以消除多重共线性,最后得出回归模型,并对模型进行了相关检验,验证模型是适合进行预测的.最后提出基于货运量是物流需求预测的关键,从三个方面提出加快河北省物流产业发展的政策建议. 相似文献
110.
Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which ‘win’ based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and ‘end-users’ to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings. 相似文献