全文获取类型
收费全文 | 469篇 |
免费 | 27篇 |
国内免费 | 31篇 |
专业分类
系统科学 | 43篇 |
丛书文集 | 18篇 |
理论与方法论 | 2篇 |
现状及发展 | 29篇 |
综合类 | 435篇 |
出版年
2023年 | 2篇 |
2022年 | 4篇 |
2021年 | 4篇 |
2020年 | 6篇 |
2019年 | 4篇 |
2018年 | 2篇 |
2017年 | 10篇 |
2016年 | 14篇 |
2015年 | 12篇 |
2014年 | 18篇 |
2013年 | 20篇 |
2012年 | 23篇 |
2011年 | 30篇 |
2010年 | 24篇 |
2009年 | 24篇 |
2008年 | 27篇 |
2007年 | 34篇 |
2006年 | 23篇 |
2005年 | 22篇 |
2004年 | 17篇 |
2003年 | 19篇 |
2002年 | 18篇 |
2001年 | 20篇 |
2000年 | 28篇 |
1999年 | 20篇 |
1998年 | 14篇 |
1997年 | 21篇 |
1996年 | 12篇 |
1995年 | 10篇 |
1994年 | 12篇 |
1993年 | 7篇 |
1992年 | 4篇 |
1991年 | 7篇 |
1990年 | 3篇 |
1989年 | 4篇 |
1988年 | 2篇 |
1987年 | 4篇 |
1986年 | 1篇 |
1982年 | 1篇 |
排序方式: 共有527条查询结果,搜索用时 15 毫秒
21.
具有连续偏差变元的二阶阻尼方程的振动定理 总被引:1,自引:1,他引:0
研究了一类具有连续偏差变元的二阶阻尼方程,得到该方程振动的新的判别准则.最后,还给出了2个例子说明本文的应用. 相似文献
22.
Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?
下载免费PDF全文
![点击此处可从《Journal of forecasting》网站下载免费的PDF全文](/ch/ext_images/free.gif)
We study the performance of recently developed linear regression models for interval data when it comes to forecasting the uncertainty surrounding future stock returns. These interval data models use easy‐to‐compute daily return intervals during the modeling, estimation and forecasting stage. They have to stand up to comparable point‐data models of the well‐known capital asset pricing model type—which employ single daily returns based on successive closing prices and might allow for GARCH effects—in a comprehensive out‐of‐sample forecasting competition. The latter comprises roughly 1000 daily observations on all 30 stocks that constitute the DAX, Germany's main stock index, for a period covering both the calm market phase before and the more turbulent times during the recent financial crisis. The interval data models clearly outperform simple random walk benchmarks as well as the point‐data competitors in the great majority of cases. This result does not only hold when one‐day‐ahead forecasts of the conditional variance are considered, but is even more evident when the focus is on forecasting the width or the exact location of the next day's return interval. Regression models based on interval arithmetic thus prove to be a promising alternative to established point‐data volatility forecasting tools. Copyright ©2015 John Wiley & Sons, Ltd. 相似文献
23.
具分段常数微分方程零解的全局吸引性 总被引:2,自引:2,他引:0
考虑具分段常数微分方程x′(t)=r(t)f(x([t])),t 0,其中r(t)非负连续,f有下界且具有负Schwarz导数,f∈C3(R,R),xf(x)<0当x≠0,f′(0)<0,[.]表示最大整数函数,证明了当-f′(0)n∫+1nr(s)ds≤2且∞∫0r(s)ds=∞时,方程的零解是全局吸引的. 相似文献
24.
粒子群优化算法中加速因子的设置与试验分析 总被引:1,自引:0,他引:1
着重分析了粒子群优化算法中线性变化加速因子对粒子收敛的影响,使用4个著名的基准函数,对加速因子进行了测试,并在此基础上,对加速因子提出了一个推荐的设置值.模拟实验结果表明,该推荐设置值可以使粒子在搜索的初期获得更好的多样性,从而使粒子具有更强的摆脱局部极值的能力,在后期加快粒子的收敛速度以提高PSO算法的性能。 相似文献
25.
26.
A long‐standing puzzle to financial economists is the difficulty of outperforming the benchmark random walk model in out‐of‐sample contests. Using data from the USA over the period of 1872–2007, this paper re‐examines the out‐of‐sample predictability of real stock prices based on price–dividend (PD) ratios. The current research focuses on the significance of the time‐varying mean and nonlinear dynamics of PD ratios in the empirical analysis. Empirical results support the proposed nonlinear model of the PD ratio and the stationarity of the trend‐adjusted PD ratio. Furthermore, this paper rejects the non‐predictability hypothesis of stock prices statistically based on in‐ and out‐of‐sample tests and economically based on the criteria of expected real return per unit of risk. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
27.
Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter
下载免费PDF全文
![点击此处可从《Journal of forecasting》网站下载免费的PDF全文](/ch/ext_images/free.gif)
This intention of this paper is to empirically forecast the daily betas of a few European banks by means of four generalized autoregressive conditional heteroscedasticity (GARCH) models and the Kalman filter method during the pre‐global financial crisis period and the crisis period. The four GARCH models employed are BEKK GARCH, DCC GARCH, DCC‐MIDAS GARCH and Gaussian‐copula GARCH. The data consist of daily stock prices from 2001 to 2013 from two large banks each from Austria, Belgium, Greece, Holland, Ireland, Italy, Portugal and Spain. We apply the rolling forecasting method and the model confidence sets (MCS) to compare the daily forecasting ability of the five models during one month of the pre‐crisis (January 2007) and the crisis (January 2013) periods. Based on the MCS results, the BEKK proves the best model in the January 2007 period, and the Kalman filter overly outperforms the other models during the January 2013 period. Results have implications regarding the choice of model during different periods by practitioners and academics. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
28.
Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters
下载免费PDF全文
![点击此处可从《Journal of forecasting》网站下载免费的PDF全文](/ch/ext_images/free.gif)
Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
29.
30.
Massimiliano Marcellino 《Journal of forecasting》2008,27(4):305-340
Predicting the future evolution of GDP growth and inflation is a central concern in economics. Forecasts are typically produced either from economic theory‐based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can hardly be beaten if they are carefully specified. However, we also identify some important cases where the adoption of a more complicated benchmark can alter the conclusions of economic analyses about the driving forces of GDP growth and inflation. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献