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In economics, the systematic treatment of data to obtain specific properties from long (or short) data series is a main objective. The use of rational models and related numerical methods can be useful to help to predict the behaviour of relevant economic variables with a certain degree of certainty. This paper is concerned with illustrating the application of several numerical methods, in particular, the corner method, epsilon-algorithm, rs-algorithm and qd-algorithm, to the problem of model identification in time series analysis. These methods, closely related to theoretical research in Pade Approximation, are proposed to identify some type of rational structure associated with economic data in different contexts (financial, marketing, farming). Moreover, they incorporate the expectations of exogenous economic variables to improve the fit and forecasting of classic time series models. |
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