排序方式: 共有86条查询结果,搜索用时 0 毫秒
41.
上证综指马尔可夫转换模型的MCMC估计和分析 总被引:2,自引:0,他引:2
引入结构突变,对上证综指马尔可夫转换-ARCH模型通过马尔可夫蒙特卡罗方法(MCMC方法)进行估计。在30000次参数模拟之后,本文得到稳健、可靠的结果,似然比检验显示本文模型好于几乎所有GARCH族模型。本文结论:(1)相对于世界主要股市,中国股市各波动状态的持续时间短、波动幅度大;(2)不像其他股市,中国股市的波动不能反应国内外的政治经济状况的变化;(3)中国股市中等波动状态的收益率显著大于0。这些结论提供了一个认识中国股市波动性的全新视角,还揭示了一种基于模型的实用数量投资方法,最后本文提出了完善中国股市的相关建议。 相似文献
42.
基于随机模拟的方法,利用可逆跳MCMC算法讨论了ARMA模型的识别和参数估计。 相似文献
43.
Crowder曾经研究过一组种子数据,这组数据所对应的模型为二项线性混合效应模型.本文运用EM算法对模型进行参数估计,并利用“点删除”方法探测到了数据中的强影响点. 相似文献
44.
针对水文模型参数不确定性分析常用方法
收敛速度缓慢,容易陷入参数空间局部最优区域等
问题,提出了PAM (parallel adaptive metropolis)
算法;对三水源新安江模型参数不确定性进行分析
研究。实例研究表明显著提高了计算速度和求解质
量,参数后验分布结果为区间预报提供了条件。 相似文献
45.
This paper develops and estimates a dynamic factor model in which estimates for unobserved monthly US Gross Domestic Product (GDP) are consistent with observed quarterly data. In contrast to existing approaches, the quarterly averages of our monthly estimates are exactly equal to the Bureau of Economic Analysis (BEA) quarterly estimates. The relationship between our monthly estimates and the quarterly data is therefore the same as the relationship between quarterly and annual data. The study makes use of Bayesian Markov chain Monte Carlo and data augmentation techniques to simulate values for the logarithms on monthly US GDP. The imposition of the exact linear quarterly constraint produces a non‐standard distribution, necessitating the implementation of a Metropolis simulation step in the estimation. Our methodology can be easily generalized to cases where the variable of interest is monthly GDP and in such a way that the final results incorporate the statistical uncertainty associated with the monthly GDP estimates. We provide an example by incorporating our monthly estimates into a Markov switching model of the US business cycle. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
46.
Cathy W. S. Chen 《Journal of forecasting》1999,18(7):505-516
We propose a solution to select promising subsets of autoregressive time series models for further consideration which follows up on the idea of the stochastic search variable selection procedure in George and McCulloch (1993). It is based on a Bayesian approach which is unconditional on the initial terms. The autoregression stepup is in the form of a hierarchical normal mixture model, where latent variables are used to identify the subset choice. The framework of our procedure is utilized by the Gibbs sampler, a Markov chain Monte Carlo method. The advantage of the method presented is computational: it is an alternative way to search over a potentially large set of possible subsets. The proposed method is illustrated with a simulated data as well as a real data. Copyright © 1999 John Wiley & Sons, Ltd. 相似文献
47.
48.
Multivariate Time Series Model with Hierarchical Structure for Over‐Dispersed Discrete Outcomes
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)
In this paper, we propose a multivariate time series model for over‐dispersed discrete data to explore the market structure based on sales count dynamics. We first discuss the microstructure to show that over‐dispersion is inherent in the modeling of market structure based on sales count data. The model is built on the likelihood function induced by decomposing sales count response variables according to products' competitiveness and conditioning on their sum of variables, and it augments them to higher levels by using the Poisson–multinomial relationship in a hierarchical way, represented as a tree structure for the market definition. State space priors are applied to the structured likelihood to develop dynamic generalized linear models for discrete outcomes. For the over‐dispersion problem, gamma compound Poisson variables for product sales counts and Dirichlet compound multinomial variables for their shares are connected in a hierarchical fashion. Instead of the density function of compound distributions, we propose a data augmentation approach for more efficient posterior computations in terms of the generated augmented variables, particularly for generating forecasts and predictive density. We present the empirical application using weekly product sales time series in a store to compare the proposed models accommodating over‐dispersion with alternative no over‐dispersed models by several model selection criteria, including in‐sample fit, out‐of‐sample forecasting errors and information criterion. The empirical results show that the proposed modeling works well for the over‐dispersed models based on compound Poisson variables and they provide improved results compared with models with no consideration of over‐dispersion. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
49.
50.
GARCH模型和SV模型的应用比较研究——以上证指数的波动性为例 总被引:1,自引:0,他引:1
顾锋娟 《浙江万里学院学报》2009,22(2):1-7
文章以上证综指2007年2月27日至2008年5月14日共293个交易日的收盘价数据为研究对象,通过构造样本外波动性预测能力指标,分析比较了GARCH类模型和SV类模型在国内市场上的适用性。文章首先估计GARCH类模型和SV类模型的参数,其中SV模型参数的估计采用最新的马尔可夫链蒙特卡罗方法(MCMC方法),并由WINBUGS软件加以实现。然后通过构造样本外预测能力指标对GARCH类模型和SV类模型的样本外预测效果进行比较,得出在国内市场SV类模型的拟合和预测效果要好于GARCH类模型。 相似文献