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Persistent link: https://www.econbiz.de/10011995775
We introduce a dynamic network model with probabilistic link functions that depend on stochastically time-varying parameters. We adopt the widely used blockmodel framework and allow the high-dimensional vector of link probabilities to be a function of a low-dimensional set of dynamic factors....
Persistent link: https://www.econbiz.de/10011566388
We study the performance of alternative methods for calculating in-sample confidence and out of-sample forecast bands for time-varying parameters. The in-sample bands reflect parameter uncertainty only. The out-of-sample bands reflect both parameter uncertainty and innovation uncertainty. The...
Persistent link: https://www.econbiz.de/10011295703
Persistent link: https://www.econbiz.de/10010527158
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We consider unobserved components time series models where the components are stochastically evolving over time and are subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of the observed time series. We develop a simulated...
Persistent link: https://www.econbiz.de/10011809984
We investigate covariance matrix estimation in vast-dimensional spaces of 1,500 up to 2,000 stocks using fundamental factor models (FFMs). FFMs are the typical benchmark in the asset management industry and depart from the usual statistical factor models and the factor models with observed...
Persistent link: https://www.econbiz.de/10011949129
This paper utilizes a very simple model to study the timing and determinants of speculationagainst a fixed exchange rate regime when investors are heterogeneous because of locationaldifferences. Location matters because resident players may incur smaller costs when takinga short-position, are...
Persistent link: https://www.econbiz.de/10011326412
We study the performance of two analytical methods and one simulation method for computing in-sample confidence bounds for time-varying parameters. These in-sample bounds are designed to reflect parameter uncertainty in the associated filter. They are applicable to the complete class of...
Persistent link: https://www.econbiz.de/10010484891
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