Likelihood-based dynamic factor analysis for measurement and forecasting
Year of publication: |
2015
|
---|---|
Authors: | Jungbacker, Borus ; Koopman, Siem Jan |
Published in: |
The econometrics journal. - Oxford : Oxford University Press, ISSN 1368-4221, ZDB-ID 1412265-0. - Vol. 18.2015, 2, p. 1-21
|
Subject: | EM algorithm | Kalman filter | Latent factors | Maximum likelihood | State space form | Zustandsraummodell | State space model | Faktorenanalyse | Factor analysis | Theorie | Theory | Prognoseverfahren | Forecasting model | Maximum-Likelihood-Schätzung | Maximum likelihood estimation | Zeitreihenanalyse | Time series analysis | Algorithmus | Algorithm | Schätzung | Estimation |
-
Likelihood-based analysis for dynamic factor models
Jungbacker, Borus, (2008)
-
Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models
Fresoli, Diego, (2023)
-
Dynamic factor models : does the specification matter?
Miranda, Karen, (2022)
- More ...
-
Koopman, Siem Jan, (2004)
-
On Importance Sampling for State Space Models
Jungbacker, Borus, (2005)
-
Dynamic Factor Models with Smooth Loadings for Analyzing the Term Structure of Interest Rates
Jungbacker, Borus, (2009)
- More ...