Bayesian panel data analysis for exploring the impact of subprime financial crisis on the US stock market
type: Item
instance of: scholarly article
title: Bayesian panel data analysis for exploring the impact of subprime financial crisis on the US stock market
zbMATH Open document ID: 1254.91626
author: Ruey S. Tsay
author: Tomohiro Ando
published in: Computational Statistics and Data Analysis
publication date: 2012-12-30T00:00:00Z
Mathematics Subject Classification ID: 91B82
Mathematics Subject Classification ID: 62P05
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62H25
zbMATH DE Number: 6119757
zbMATH Keywords: panel data
zbMATH Keywords: Markov chain Monte Carlo
zbMATH Keywords: model selection
MaRDI profile type: Publication
full work available at URL: j.csda.2010.11.028
OpenAlex ID: W2056123184
cites work: Using principal components for estimating logistic regression with high-dimensional multicollinear data
cites work: A new look at the statistical model identification
cites work: Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models
cites work: Bayesian factor analysis with fat-tailed factors and its exact marginal likelihood
cites work: Model selection for generalized linear models with factor-augmented predictors
cites work: Quantile regression models with factor‐augmented predictors and information criterion
cites work: Inferential Theory for Factor Models of Large Dimensions
cites work: Determining the Number of Factors in Approximate Factor Models
cites work: Q3861108
cites work: Q3125703
cites work: High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics
cites work: Distribution of the generalised inverse of a random matrix and its applications
cites work: Common risk factors in the returns on stocks and bonds
cites work: Q4807277
cites work: Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics
cites work: Q4865942
cites work: Q4298922
cites work: Model choice: a minimum posterior predictive loss approach
cites work: Q4864274
cites work: Q3136286
cites work: Determining the Number of Factors in the General Dynamic Factor Model
cites work: Forecast comparison of principal component regression and principal covariate regression
cites work: Natural conjugate priors for the instrumental variables regression model applied to the Angrist-Krueger data
cites work: Q4127803
cites work: Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration
cites work: Generalized reduced rank tests using the singular value decomposition
cites work: Prediction of multivariate responses with a selected number of principal components
cites work: Efficient Posterior Simulation for Cointegrated Models with Priors on the Cointegration Space
cites work: Q4461333
cites work: Spatial dynamic factor analysis
cites work: Modelling high-dimensional data by mixtures of factor analyzers
cites work: Estimating the dimension of a model
cites work: Principal component regression for data containing outliers and missing elements
cites work: On Bayesian principal component analysis
cites work: Bayesian Measures of Model Complexity and Fit
cites work: Forecasting Using Principal Components From a Large Number of Predictors
cites work: Bayesian Inference in CointegratedI(2) Systems: A Generalization of the Triangular Model
cites work: Bayesian analysis of the error correction model
cites work: Markov chains for exploring posterior distributions. (With discussion)
DOI: 10.1016/J.CSDA.2010.11.028
description: scientific article; zbMATH DE number 6119757