## Applications of Computer Aided Time Series ModelingThis 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. |

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### Contents

The SSATS algorithm and subspace methods | 3 |

A guide to state space modeling of multiple time series | 15 |

Evaluating state space forecasts of soybean complex prices | 75 |

Copyright | |

9 other sections not shown

### Other editions - View all

Applications of Computer Aided Time Series Modeling Masanao Aoki,Arthur M. Havenner Limited preview - 2012 |

Applications of Computer Aided Time Series Modeling Masanao Aoki,Arthur M Havenner No preview available - 1996 |

### Common terms and phrases

actual algorithm Analysis Aoki Applications approximation average calculated changes coefficients column combined components computed correlation covariance cycle model daily decomposition defined determined direction DJIA dynamics Economic eigenvalues equation error estimates example exchange rate exogenous expected extended Figure forecast four function Futures given Havenner Hidden Units horizon identification in-sample indicates initial input interest Journal labor linear matrix mean methods month monthly NMSE observations out-of-sample output pair parameter performance period portfolio positive possible prediction presented problem procedure produce Quarterly random ratio realized residuals returns roots sample seasonal series models shown signal significant simulated singular values space model specification squares standard stationary statistics stochastic structural Table theory Tokyo trading trend univariate utility variables variance vector zero