Applications of Computer Aided Time Series Modeling I: Introduction to State Space Modeling 1. The SSATS algorithm and subspace methods 2. A guide to state space modeling of multiple time series II: Applications of State Space Algorithm 1. Evaluating state space forecasts of soybean complex prices 2. A state space model of monthly US wheat prices 3. Managing the heard: price forecasts for California cattle production 4. Labor market and cyclical fluctuations 5. Modeling cointegrated processes by a vector valued state space algorithm 6. A method for identification of combined deterministic stochastic systems 7. Competing exchange rate models 8. Application of state space models to ocean climate varibility in the northeast pacific ocean III: Applications of Neural Networks 1. On the equivalence between ARMA models and simple recurrent neural networks 2. Forecasting stock market indicies with recurrent neural networks. This book consists of three parts: Part One is composed of two introductory chapters. The first chapter provides an instrumental varible interpretation of the state space time series algorithm originally proposed by Aoki (1983) and gives an introductory account for incorporating exogenous signals in state space models. The second chapter by Havenner gives practical guidance in apply­ ing this algorithm by one of the most experienced practitioners of the method. Havenner begins by summarizing six reasons state space methods are advanta­ geous and then walks the reader through construction and evaluation of a state space model for four monthly macroeconomic series: industrial production in­ dex consumer price index six month commercial paper rate and money stock (Ml). To single out one of the several important insights in modeling that he shares with the reader he discusses in Section 2ii the effects of sampling er­ rors and model misspecification on successful modeling efforts. He argues that model misspecification is an important amplifier of the effects of sampling error that may cause symplectic matrices to have complex unit roots a theoretical impossibility. Correct model specifications increase efficiency of estimators and often eliminate this finite sample problem. This is an important insight into the positive realness of covariance matrices; positivity has been emphasized by system engineers to the exclusion of other methods of reducing sampling error and alleviating what is simply a finite sample problem. The second and third parts collect papers that describe specific applications.