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排序方式: 共有626条查询结果,搜索用时 15 毫秒
1.
In this paper, I assess the evidence for a structural break in labor productivity growth in the years before the Great Recession with the use of out-of-sample forecasting exercises for the years 2010 to 2019 and the recently developed Beveridge–Nelson filter. Models based on a Beveridge–Nelson filter with no structural breaks outperform those allowing for a structural break, and there is statistically significant evidence that they outperform the random walk, though all models were too optimistic about labor productivity growth. Recently developed statistical tests do point to the presence of a structural break before the Great Recession, but uncertainty about the data-generating process for labor productivity growth or the timing and magnitude of the break may be too great to be helpful in forecast preparation. 相似文献
2.
The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution. 相似文献
3.
指数滤波器是一类新构造出来的输出信噪比和目标时延分辨力随指数变化的滤波器,该滤波器在损失一定输出信噪比的前提下可以有效提高目标时延分辨力,从而提高目标时延估计精度,但仅采用单个指数滤波器仍存在输出信噪比和目标时延分辨力均达不到实际需求的情况。在乘积型高阶模糊函数乘积运算的启发下,在指数滤波器的基础上提出了一种新的乘积型指数滤波器,并分析了该乘积型指数滤波器的输出信噪比及目标时延分辨力等性能。仿真实验表明,所提的乘积型指数滤波器在低信噪比情况下可以更有效提高多目标时延估计精度,且算法简单易于实现,适用于背景复杂的多目标参数估计任务。 相似文献
4.
Marco Realdon 《Quantitative Finance》2019,19(2):191-210
Prior literature indicates that quadratic models and the Black–Karasinski model are very promising for CDS pricing. This paper extends these models and the Black [J. Finance 1995, 50, 1371–1376] model for pricing sovereign CDS’s. For all 10 sovereigns in the sample quadratic models best fit CDS spreads in-sample, and a four factor quadratic model can account for the joint effects on CDS spreads of default risk, default loss risk and liquidity risk with no restriction to factors correlation. Liquidity risk appears to affect sovereign CDS spreads. However, quadratic models tend to over-fit some CDS maturities at the expense of other maturities, while the BK model is particularly immune from this tendency. The Black model seems preferable because its out-of-sample performance in the time series dimension is the best. 相似文献
5.
我国大麦价格波动特征及其影响因素分析 总被引:1,自引:0,他引:1
[目的]大麦价格剧烈波动会直接影响大麦种植户的生产积极性和大麦产业的平稳发展,研究大麦价格波动特征及其影响因素,有助于提升大麦产业链相关主体识别和应对市场风险的能力,促进大麦产业的健康发展。[方法]文章先采用HP滤波法和ARCH类模型分析了2011年4月至2017年2月我国大麦价格波动特征,然后采用脉冲响应函数分析了我国大麦价格波动影响因素。[结果]我国大麦价格波动存在明显的季节性和周期性,样本期内总体上呈现逐渐下降趋势;我国大麦价格具有显著的波动集聚性,我国大麦价格具有显著的不对称性;在该文选择的影响因素中,大麦进口量和国际大麦价格是影响我国大麦价格波动的主要因素。[结论]该文提出必须保障并提高国内大麦合理产能、完善大麦价格监测预警体系、加强国内大麦进口企业整合和推动大麦进口来源多元化的政策建议。 相似文献
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由于农业保险所承保的农业自然灾害具有统计学上的不可预测性,灾害学上的时空延展性和经济学上的不可控性,使得经营农业保险的保险公司面临较高赔付风险. 为测算我国农业保险的赔付风险度,收集整理1984~2012年农业保险与财产保险赔付率数据,采用H-P滤波分解法对其进行长期趋势与短期波动的分解和比较分析. 研究发现:我国农业保险整体赔付水平和赔付的波动幅度远高于财产保险,验证了农业保险具有高风险经营的特性. 但是近年来,农业保险长期赔付趋势呈现平稳略微下降态势,短期波动幅度逐步收敛. 未来随着经营主体的增加,农业保险经营中须把握保险保障度的提升与保险公司经营风险管控的有效平衡;农业保险的适当盈利性与福利改进的有效融合. 相似文献
8.
传统粒子滤波(PF)直接采用状态转移先验分布作为重要性密度函数来近似后验概率密度函数,使得后验概率密度函数未包含量测信息。针对此问题,提出了一种改进高阶容积粒子滤波(CPF)的系统状态估计算法。算法采用七阶正交容积卡尔曼滤波(7th-CQKF)对PF的粒子进行传递,使得先验分布更新阶段融入最新量测信息;通过7th-CQKF设计重要性密度函数,提高对状态后验概率密度的逼近程度;通过反比例函数计算粒子权重,突出大噪声粒子与小噪声粒子权重差别,提高粒子有效性。仿真结果表明,改进高阶容积粒子滤波的估计精度高于容积粒子滤波(CPF)。 相似文献
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10.
Didier Nibbering Richard Paap Michel van der Wel 《International Journal of Forecasting》2018,34(2):288-311
This paper studies what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into trend, business cycle, and irregular components. We examine which components are captured by professional forecasters by regressing their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods, we find that, in the short run, the Survey of Professional Forecasters can predict almost all of the variation in the time series due to the trend and the business cycle, but that the forecasts contain little or no significant information about the variation in the irregular component. 相似文献