Maximum Likelihood Estimation for Generalized Autoregressive Score Models
By Francisco Blasques, S. J. Koopman, André Lucas. 2014
73 citations recorded by the source corpus.
Subfields: Finance, Global and Planetary Change, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/4c1d56bf2a5a7d041a5c06a3eff21f99b2c3b6d4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
- S. J. Koopman
- Francisco Blasques
- André Lucas
- M. Bazzi
- Julia Schaumburg
- Paolo Gorgi
- Olivier Wintenberger
- Rutger Lit
In Global and Planetary Change
In Statistics and Probability
- André Lucas
- Francisco Blasques
- S. J. Koopman
- M. Bazzi
- Julia Schaumburg
- Olivier Wintenberger
- Paolo Gorgi
- Tommaso Proietti
Citations in the corpus
- Feasible invertibility conditions and maximum likelihood estimation for observation-driven models (2018, background): “We find that, in general, the typical invertibility conditions needed to ensure the consistency of the ML estimator, which are considered for instance in [43], [42] and [5], lead often to a parameter region that is too small for practical purposes.”
- Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models (2016, methodology): “The expressions may seem complicated at first, but we show below that their verification is often straightforward; see also Blasques et al. (2014b) for the verification of similar moment conditions in a wide range of observation-driven models.”
- Information Theoretic Optimality of Observation Driven Time Series Models (2014, unknown): “Furthermore, the score driven class of models encompasses many familiar observation driven time-varying parameter models such as the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the autoregressive conditional duration model of Engle a”
- Time‐Varying Transition Probabilities for Markov Regime Switching Models (2017, methodology): “The SE nature of the filtered sequence is also an important ingredient in obtaining proofs of consistency and asymptotic normality of the maximum likelihood estimator that rely on an application of laws of large numbers and central limit theorems; see Blasques et al. (2014a).”