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1.
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.
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   
3.
We assess the conditional relationship in the time-frequency domain between the return on S&P 500 and confirmed cases and deaths by COVID-19 in Hubei, China, countries with record deaths and the world, for the period from January 29 to June 30, 2020. Methodologically, we follow Aguiar-Conraria et al. (2018), by using partial coherencies, phase-difference diagrams, and gains. We also perform a parametric test for Granger-causality in quantiles developed by Troster (2018). We find that short-term cycles of deaths in Italy in the first days of March, and soon afterwards, cycles of deaths in the world are able to lead out-of-phase US stock market. We find that low frequency cycles of the US market index in the first half of April are useful to anticipate in an anti-phasic way the cycles of deaths in the US. We also explore sectoral contagion, based on dissimilarities, Granger causality and partial coherencies between S&P sector indices. Our findings, such as the strategic role of the energy sector, which first reacted to the pandemic, or the evidence about predictability of the Telecom cycles, are useful to tell the history of the pass-through of this recent health crises across the sectors of the US economy.  相似文献   
4.
The current literature has examined the effect of investor sentiment on energy prices, but no study ever has explored the validity of the reverse question. Therefore, this article explore whether energy prices (i.e., crude oil and natural gas prices) affect U.S. investor sentiment, using the methodology of quantile regression. The empirical results document that controlling for a number of U.S. macroeconomic and financial factors, there exists a statistically significant association between oil and natural gas prices and investor sentiment. However, only natural gas prices appear to retain their statistical significance over the majority of quantiles. These findings received robust support under alternative measures of the investor sentiment index.  相似文献   
5.
This study provides evidence for the impact of an urban growth boundary (UGB) on house prices. The study employs a two-stage quantile spatial regression method on a dataset that inventories sales transactions of single-family houses within two miles of either side of the eastern boundary of the primary UGB in King County, Washington. The results show that while the UGB decreases house prices across the entire house price spectrum, the impact is uneven; it is most pronounced for houses in the 5th to 8th decile of prices. These findings should encourage policy makers to adopt sub-housing-market-level policy approaches to address UGB and other urban and regional development policies’ potential impacts on house prices.  相似文献   
6.
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   
7.
The paper provides evidence that fiscal rules can limit the political budget cycle. It uses data on Italian municipalities during the 2000s and shows that: 1) municipalities are subject to political budget cycles in capital spending; 2) the Italian sub-national fiscal rule (Domestic Stability Pact, DSP) introduced in 1999 has been enforced by the central government; 3) municipalities subject to the fiscal rule show more limited political budget cycles than municipalities not subject to the rule. In order to identify the effect, we rely on the fact that the domestic fiscal rule does not apply to municipalities below 5000 inhabitants. We find that the political budget cycle increases real capital spending by about 10–20 percent on average in the years prior to municipal elections and that municipalities subject to the DSP show a pre-electoral increase in capital spending which is only a quarter of the one of municipalities not subject to the rule.  相似文献   
8.
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   
9.
ABSTRACT

We examine gender wage gap (GWG) in Greece for 2013, by using a survey data set. Our findings show first, that the unadjusted GWG is 15.3%, while European Commission reports a value of 15%. Secondly, we derive the ‘adjusted’ GWG, using the Oaxaca and Ransom (OR) and the Juhn, Murphy and Pierce (JMP) methods to be ranging from 10% to 13.6%. Thirdly, looking into the behaviour of the full population, we find a decreasing trend for the discrimination effects, an increasing trend for the residuals effects and a ‘random’ endowments effects while moving to higher deciles. These three effects are associated to the economic crisis 2008–2015. Fourthly, our findings do not show evidence of either a ‘glass ceiling’ effect or a ‘sticky floor’ effect. Finally, that there is strong evidence that investing in higher education reduces the wage discrimination between sexes.  相似文献   
10.
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   
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