Showing 1 - 10 of 269
We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...
Persistent link: https://www.econbiz.de/10013049149
A new model for time-varying spatial dependencies is introduced. It forms an extension to the popular spatial lag model and can be estimated conveniently by maximum likelihood. The spatial dependence parameter is assumed to follow a generalized autoregressive score (GAS) process. The theoretical...
Persistent link: https://www.econbiz.de/10010491085
Persistent link: https://www.econbiz.de/10011705251
We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...
Persistent link: https://www.econbiz.de/10010391531
We characterize the dynamic properties of Generalized Autoregressive Score (GAS) processes by identifying regions of the parameter space that imply stationarity and ergodicity. We show how these regions are affected by the choice of parameterization and scaling, which are key features of GAS...
Persistent link: https://www.econbiz.de/10010326396
We investigate the information theoretic optimality properties of the score function of the predictive likelihood as a device to update parameters in observation driven time-varying parameter models. The results provide a new theoretical justification for the class of generalized autoregressive...
Persistent link: https://www.econbiz.de/10013055616
We describe stationarity and ergodicity (SE) regions for a recently proposed class of score driven dynamic correlation models. These models have important applications in empirical work. The regions are derived from sufficiency conditions in Bougerol (1993) and take a non-standard form. We show...
Persistent link: https://www.econbiz.de/10010326270
We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. We propose a general and efficient likelihood evaluation method for this class of models via the combination of numerical and Monte Carlo integration methods. Our methodology explores the idea that...
Persistent link: https://www.econbiz.de/10010325813
We propose a new model for dynamic volatilities and correlations of skewed and heavy-tailed data. Our model endows the Generalized Hyperbolic distribution with time-varying parameters driven by the score of the observation density function. The key novelty in our approach is the fact that the...
Persistent link: https://www.econbiz.de/10010326055
We study whether and when parameter-driven time-varying parameter models lead to forecasting gains over observation-driven models. We consider dynamic count, intensity, duration, volatility and copula models, including new specifications that have not been studied earlier in the literature. In...
Persistent link: https://www.econbiz.de/10010326198