Multiple input transfer function noise modelling in the time domain, Empirical evidence on Scandinavian stock data

Provides evidence on the power of transfer function noise modelling in explaining the empirical connection between endogenous and exogenous (control) variables in linear regression type input‐output systems. The multiple input transfer function noise model – of specific value when the input variables are collinear – is used to demonstrate the connection between macroeconomic forces and stock market pricing on a thin security market. Shows that the transfer function approach provides new evidence partly in conflict with previous results obtained by ordinary least squares methodology. Previous empirical evidence suggests that money supply, inflation, the level of industrial production and the psychological impact of the general index of the Stockholm Stock Exchange affects Finnish stock pricing. The problem of selecting relevant economic state variables is tackled by regressing each of the five factor time series obtained from testing the arbitrage pricing theory (see Östermark, circa 1989) on the set of tentative state variables. The economic state variables are significant explanators of stock pricing, both at the market and at the individual asset level. Only nine individual stocks are tested. Comprehensive testing of all individual stocks is left for future research.