Titlebook: Continuous Time Modeling in the Behavioral and Related Sciences; Kees van Montfort,Johan H. L. Oud,Manuel C. Voelkl Book 2018 Springer Int

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Stochastic Differential Equation Models with Time-Varying Parameters,uman dynamic processes with self-organizing features comprise subprocesses that unfold across multiple time scales. Incorporating time-varying parameters (TVPs) into a dynamic model of choice provides one way of representing self-organization as well as multi-time scale processes. Extant application

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Recursive Partitioning in Continuous Time Analysis,rrelations cannot be made. Machine learning-inspired approaches have been gaining momentum in modeling such “big” data because they offer a systematic approach to searching for potential interrelationships among variables. In practice, researchers may often start with a small model strongly guided b

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Continuous versus Discrete Time Modeling in Growth and Business Cycle Theory,he basic Solow and Ramsey models of growth and the business cycle toward the issue of equilibrium indeterminacy and endogenous cycles. In this paper, we introduce some of those relevant issues in economic dynamics. First, we describe a baseline continuous versus discrete time modeling setting releva

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Continuous Time State Space Modelling with an Application to High-Frequency Road Traffic Data,e models is that time gaps between consecutive observations in a time series are allowed to vary throughout the process. We discuss some essential details of the continuous time state space methodology and review the similarities and the differences between the continuous time and discrete time appr

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Implementation of Multivariate Continuous-Time ARMA Models,utational implementation of a stationary normal multivariate CARMA model is illustrated. A review of a parametric setup is shown. Data are assumed to be observed at irregular non-synchronous discrete time points. The computational approach for calculating the likelihood is based on a state-space for