MODELING DYNAMIC-SYSTEMS WITH BIASED REGRESSION AND SPECTRAL METHODS

Considers the modelling of dynamic systems using biased regression and spectral methods. Provides evidence on the power of transfer function modelling for unravelling the empirical connection between endogenous and exogenous (control) variables in both regression type and spectral input-output systems. The Multiple Input Transfer Function Noise Model - of specific value when the input variables are collinear - has previously been used to demonstrate the connection between macroeconomic forces and stock market pricing on a thin security market. Compares the adequacy of representative time and frequency domain algorithms for modelling observed data series. The estimations are done with the combined Transfer Function and Cartesian ARIMA Search algorithm of Ostermark and Hoglund and the CAPM/APM programs of Ostermark.