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Time Series Analysis by State Space Methods
TLDR
This excellent text provides a comprehensive treatment of the state space approach to time series analysis, where observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbence terms, each of which is modelled separately.
GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS
TLDR
A unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models, referred to as Generalized Autoregressive Score (GAS) models, which encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity.
A simple and efficient simulation smoother for state space time series analysis
TLDR
A new technique for simulation smoother in state space time series analysis is presented which is both simple and computationally efficient and includes models with diffuse initial conditions and regression effects.
Monte Carlo maximum likelihood estimation for non-Gaussian state space models
State space models are considered for observations which have non-Gaussian distributions. We obtain accurate approximations to the loglikelihood for such models by Monte Carlo simulation. Devices are
Statistical algorithms for models in state space using SsfPack 2.2
TLDR
It is shown that SsfPack can be easily used for implementing, fitting and analysing Gaussian models relevant to many areas of econometrics and statistics.
Time series analysis of non‐Gaussian observations based on state space models from both classical and Bayesian perspectives
TLDR
Methods for estimating conditional and posterior means of functions of the state vector given the observations, and the mean square errors of their estimates, are developed and extended to cover the estimation of conditional and anterior densities and distribution functions.
Stamp 5.0 : structural time series analyser, modeller and predictor
TLDR
Part 1: installation procedure for STAMP; part 2: tutorials on structural time series modelling; and part 6: general information STAMP manuals.
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts
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