Maximum likelihood estimation for dynamic factor models with missing data

DOI10.1016/j.jedc.2011.03.009zbMath1217.91153OpenAlexW2093281401MaRDI QIDQ550846

B. Jungbacker, Siem Jan Koopman, Michel van der Wel

Publication date: 13 July 2011

Published in: Journal of Economic Dynamics \& Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jedc.2011.03.009

zbMATH Keywords

high-dimensional vector seriesKalman filtering and smoothingunbalanced panels of time series

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Censored data models (62N01) Economic time series analysis (91B84)

Related Items

Robust and nonlinear control literature survey (No. 26) ⋮ Multi-step prediction of time series with random missing data ⋮ A FLEXIBLE STATE SPACE MODEL AND ITS APPLICATIONS ⋮ Factor-based imputation of missing values and covariances in panel data of large dimensions ⋮ Large dimensional latent factor modeling with missing observations and applications to causal inference ⋮ Generalized dynamic panel data models with random effects for cross-section and time ⋮ Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data ⋮ On factor models with random missing: EM estimation, inference, and cross validation ⋮ Monetary, fiscal and oil shocks: evidence based on mixed frequency structural FAVARs ⋮ Dynamic principal component analysis with missing values ⋮ BLIND SIGNAL SEPARATION OF MIXTURES OF CHAOTIC PROCESSES: A COMPARISON BETWEEN INDEPENDENT COMPONENT ANALYSIS AND STATE SPACE MODELING ⋮ Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components

Uses Software

• astsa

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A two-step estimator for large approximate dynamic factor models based on Kalman filtering
• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models
• Extensions of estimation methods using the EM algorithm
• A parametric estimation method for dynamic factor models of large dimensions
• Numerical Optimization
• Filtering and smoothing of state vector for diffuse state-space models
• Forecasting Using Principal Components From a Large Number of Predictors
• Evaluation of likelihood functions for Gaussian signals
• Inferential Theory for Factor Models of Large Dimensions
• Monte Carlo Likelihood Estimation for Three Multivariate Stochastic Volatility Models
• Linear Dynamic Recursive Estimation from the Viewpoint of Regresion Analysis