Optimal prediction with conditionally heteroskedastic factor analysed hidden Markov models
DOI10.1007/s10614-009-9181-7zbMath1176.91127OpenAlexW2059305548MaRDI QIDQ1037440
Mohamed Saidane, Christian Lavergne
Publication date: 16 November 2009
Published in: Computational Economics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10614-009-9181-7
HMMEM algorithmconditional heteroskedasticityforecastinglatent factor modelstime series segmentation
Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Markov processes: estimation; hidden Markov models (62M05) Economic time series analysis (91B84)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
- A structured variational learning approach for switching latent factor models
- Model specification tests. A simultaneous approach
- Dual adaptive control of nonlinear stochastic systems using neural networks
- ARCH modeling in finance. A review of the theory and empirical evidence
- Estimating the dimension of a model
- Testing for GARCH effects: A one-sided approach
- Dynamic linear models with Markov-switching
- Autoregressive conditional heteroskedasticity and changes in regime
- Generalized autoregressive conditional heteroscedasticity
- The consistency of the BIC Markov order estimator.
- Likelihood ratio inequalities with applications to various mixtures
- Regime switching in foreign exchange rates: Evidence from currency option prices
- Conditionally heteroscedastic factorial HMMs for time series in finance
- Econometric Evaluation of Linear Macro-Economic Models
- Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances
- On a measure of lack of fit in time series models
- Practical Bayesian Density Estimation Using Mixtures of Normals
- On a Mixture Autoregressive Conditional Heteroscedastic Model
- The likelihood ratio test for the number of components in a mixture with Markov regime
- Quadratic ARCH Models
- Identification, estimation and testing of conditionally heteroskedastic factor models
This page was built for publication: Optimal prediction with conditionally heteroskedastic factor analysed hidden Markov models