.png){kind=icon}
返回
Bayesian Forecasting for a Logistic Mixture Double Autoregressive Model
.png){kind=icon}
PRE
AI
DOI：10.1002/for.70109.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To capture the dynamic relationship between financial time series and covariates, we consider a logistic mixture double autoregressive model with explanatory variables. The model is composed of two double autoregressive models whose mixing ratio is time-varying and is driven by a logistic regression structure. By introducing a series of Bernoulli distributed latent variables, a complete data likelihood is obtained, which makes the Bayesian inference feasible. Based on this likelihood, a new Markov chain Monte Carlo algorithm is developed to address the parameter estimation problem. The heteroscedasticity test problem for the underlying process is also addressed by means of Bayes factor. The performances of the proposed methods are evaluated via simulations. Finally, the proposed model is applied to the Shanghai Stock Exchange Index data set.
Keyword:
Bayes factor
explanatory variables
financial time series
mixture double autoregressive model
VTMCMC algorithm
期刊
IF:
2.7
论文数:
2.3K
被引数:
3.0K
机构
引用论文
A Likelihood Ratio Test of a Homoscedastic Multivariate Normal Mixture Against a Heteroscedastic Multivariate Normal Mixture同方差多元正态混合模型与异方差多元正态混合模型的似然比检验
OAAI
.png){kind=icon}
OAAI分享收藏分享收藏分享收藏分享收藏Full Bayesian analysis of seasonal autoregressive models under scale-mixtures of normal errors季节自回归模型在尺度混合正态误差下的完全贝叶斯分析
PREAI
分享收藏
查看更多内容