Modeling nonlinear processes with generalized autoregressions

AbstractA general procedure for modeling stochastic, nonlinear, dynamic process from time series data is proposed. The approach represents a natural generalization of linear autoregressions. The derivation of a state space representation from the resulting difference-equation model is discussed