Modeling nonlinear processes with generalized autoregressions

Modeling Expectations with Noncausal Autoregressions

This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. We argue that noncausal autoregressive models are especially well suited for modeling expectations. Unlike conventional causal autoregressive models, they explicitly show how the considered economic variable is a¤ected by expectations and how expectations are formed. ...

Macroeconomic Modeling with Asymmetric Vector Autoregressions

Modeling Sparse Generalized Longitudinal Observations With Latent Gaussian Processes

In longitudinal data analysis one frequently encounters non-Gaussian data that are repeatedly collected for a sample of individuals over time. The repeated observations could be binomial, Poisson or of another discrete type or could be continuous. The timings of the repeated measurements are often sparse and irregular. We introduce a latent Gaussian process model for such data, establishing a c...

Generalized linearization in nonlinear modeling of data

The principal innovative idea in this note is to transform the original complex nonlinear modeling problem into a combination of linear problem and very simple nonlinear problems. The key step is the generalized linearization of nonlinear terms. This paper only presents the introductory strategy of this methodology. The practical numerical experiments will be provided subsequently.

COVARIANCE MATRIX OF MULTIVARIATE REWARD PROCESSES WITH NONLINEAR REWARD FUNCTIONS

Multivariate reward processes with reward functions of constant rates, defined on a semi-Markov process, first were studied by Masuda and Sumita, 1991. Reward processes with nonlinear reward functions were introduced in Soltani, 1996. In this work we study a multivariate process , , where are reward processes with nonlinear reward functions respectively. The Laplace transform of the covar...