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1.
《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
2.
作为互联网理财产品的代表,余额宝收益率与货币市场基准利率密切相关。本文 选取余额宝收益率与市场利率的代表——上海银行间同业拆借利率(Shibor)的数据,采用向 量自回归模型(VAR)对两者的关系进行实证研究,研究结果表明:余额宝收益率与Shibor互 为因果,余额宝以上万亿的基金规模已经能够影响Shibor,当期的余额宝收益率和Shibor主要受 自身前期影响。Shibor的市场基准性仍需进一步加强;余额宝应加强自身经营,充分利用大数 据技术建立风险防范机制,提升风险管理效率;相关部门应重视对余额宝的监管,既要保证监 管的有效性,又要适度监管,为金融创新留下空间,维护金融市场的健康稳定发展。 相似文献
3.
We test whether a voter's decision to cast a vote depends on its probability of affecting the election outcome. Using exogenous variation arising at population cutoffs determining council sizes in Finnish municipal elections, we show that larger council size increases both pivotal probabilities and turnout. These effects are statistically significant, fairly large and robust. Finally, we use a novel instrumental variables design to show that the jumps in the pivotal probabilities are the likely candidate for explaining the increase in turnout, rather than the other observed simultaneous jumps at the council size cutoffs. Moreover, our results indicate that turnout responds only to within-party pivotal probabilities, perhaps because they are more salient to the voters than the between-party ones. 相似文献
4.
《International Journal of Forecasting》2019,35(4):1288-1303
Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period. 相似文献
5.
《International Journal of Forecasting》2022,38(1):193-208
There are two potential directions of forecast combination: combining for adaptation and combining for improvement. The former direction targets the performance of the best forecaster, while the latter attempts to combine forecasts to improve on the best forecaster. It is often useful to infer which goal is more appropriate so that a suitable combination method may be used. This paper proposes an AI-AFTER approach that can not only determine the appropriate goal of forecast combination but also intelligently combine the forecasts to automatically achieve the proper goal. As a result of this approach, the combined forecasts from AI-AFTER perform well universally in both adaptation and improvement scenarios. The proposed forecasting approach is implemented in our R package AIafter, which is available at https://github.com/weiqian1/AIafter. 相似文献
6.
Modelling lottery sales as a function of the mean, standard deviation and skewness of the probability distribution of returns potentially gives insights into how the design of a game could be modified to maximise net revenue. But use of OLS is problematic because the level of sales itself affects values of the moments (and insufficient instruments are available for IV regression). We draw on the concept of a rational expectations equilibrium, developing a new regression model which corrects for endogeneity where the causal impact of the dependent variable on the right-hand side variables is deterministic. We apply the model to data on lotto sales from Spain. Using the Spanish data, we show that results provide more reliable guidance to lottery agencies because accounting for endogeneity leads to significantly different results from OLS and these results have superior performance in out-of-sample forecasting of sales. More generally, results prove consistent with the Friedman-Savage explanation of why people buy lottery tickets and with evidence from racetrack data that ‘bettors love skewness’. 相似文献
7.
The study ascertained the impact of the Fast Track Land Reform Programme (FTLRP) in Zimbabwe on tobacco production. The Chow Test, Vector Error Correction Model (VECM) and the Vector Error Granger-Causality Test were applied. The results reveal that there was a structural break in tobacco sales in the year 2000. Furthermore, in the long-run, area under tobacco production had a positive impact whilst number of tobacco producers had a negative impact on tobacco sold pre-FTLRP. Post-FTLRP, area of tobacco and number of tobacco producers had negative impact. In addition, the FTLRP induced an 8.94 % increase in the speed of adjustment in correcting the long-run equilibrium in tobacco sales. In the short-run, the FTLRP caused a percentage increase in the area of tobacco production and number of tobacco producers to induce a 0.65 % and 0.76 % increase in the tobacco sales, respectively. Area of tobacco production and number of tobacco producers Granger-caused tobacco sales in the pre-FTLRP period. Post-FTLRP, the number of tobacco growers Granger-caused tobacco sales. It is concluded that the FTLRP had an impact on tobacco sales, mainly through the number of tobacco growers. The study recommends the specialisation and training of the new farmers to improve productivity. 相似文献
8.
《International Journal of Forecasting》2020,36(3):1092-1113
Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks. 相似文献
9.
ABSTRACT A panel smooth transition regression model was adopted to analyse the non-linear impact of oil prices on oil demand. Data for 42 countries was obtained from the International Energy Agency for the time period spanning from January 1990 to June 2017. The results indicate that a threshold value does exist. Furthermore, when the oil price was lower than this threshold value, a positive relationship between oil price and oil demand was observed. When the price of oil was higher than the threshold value, however, a negative relationship between price and demand was found. 相似文献
10.
Carla Gonçalves Ricardo J. Bessa Pierre Pinson 《International Journal of Forecasting》2021,37(1):322-342
Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations. 相似文献