Likelihood-based Analysis for Dynamic Factor Models

By Borus Jungbacker, S. J. Koopman. 2008

122 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2122c3fadbfef76f0b657535b9b4affe6e1c9417

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Citations in the corpus

Identification and Estimation of Dynamic Factor Models (2012, background): “In the literature, the maximum likelihood estimation of dynamic factor models has been considered by many authors, such as Watson and Engle (1983), Quah and Sargent (1993), Jungbacker and Koopman (2008), and Doz et al. (2011).”
Sharing a Ride on the Commodities Roller Coaster: Common Factors in Business Cycles of Emerging Economies (2015, background): “Following Geweke and Zhu (1996) and Jungbacker and Koopman (2008) we postulate a latent factor, fCot , that evolves according to an AR(1) process: fCot = φCof Co t−1 + σ fCoεf Co t , ε fCo t ∼ N(0, 1) (14) The (country-specific) commodity price, pCoj,t , is related to the common factor as…”
GLS Estimation of Dynamic Factor Models (2011, methodology): “In a related paper, Jungbacker and Koopman (2008) consider the state space representation of the factor model,”
Real-Time Measurement of Business Conditions (2007, unknown): “[Perhaps the methods of Jungbacker and Koopman (2008) could be used to improve the speed of our gradient evaluation, although we have not yet explored that avenue.”
Identification and Bayesian Estimation of Dynamic Factor Models (2015, background): “In the literature, the maximum likelihood estimation of dynamic factor models has been considered by many authors, such as Geweke (1977), Watson and Engle (1983), Quah and Sargent (1993), Kim and Nelson (1999), Junbacker and Koopman (2008), and Doz, Giannone, and Reichlin (2012).”
A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models (2012, unknown): “Recent results by Jungbacker and Koopman (2008) show that computational e‐ciency can be further improved.”
The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve (2008, methodology): “Jungbacker and Koopman (2007) provided new, computationally efficient methods for implementing the Kalman filter for dynamic factor models that are applicable to the FSN–ECM model and are ideally suited to the large N m case just described.”
Variable-Centered, Person-Centered, and Person-Specific Approaches (2018, background): “…within-subject, and time-lagged relationships of latent and/or measured variables (Chow et al., 2010; Gu, Preacher, & Ferrer, 2014; Ram et al., 2013), and dynamic factor analysis may even be considered a special instance of state-space modeling (Jungbacker & Koopman, 2008; Molenaar, 2003).”
Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions (2010, unknown): “8 See Doz et al. (2006), Jungbacker and Koopman (2008) , and Bai and Ng (2008).”
Maximum Likelihood Estimation of Factor Models on Data Sets with Arbitrary Pattern of Missing Data (2010, methodology): “For example, Proietti (2008) or Jungbacker and Koopman (2008) restrict Λ as:16 Λ = [ Ir Λ∗ ] , where Λ∗ is (n−r)×r unrestricted matrix.”
A parametric estimation method for dynamic factor models of large dimensions (2009, background): “Recently, there appeared a few unpublished papers dealing with ML estimation of large N- parametric factor models (see in particular, Doz et al., 2006, 2007 and Jungbacker and Koopman, 2008).”