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We use wavelet multiresolution decomposition and cross-wavelet analysis to reveal certain properties in financial data related to mortgages to households and gross domestic product data in Spain. Wavelet techniques possess many desirable properties, some of which are useful as a vehicle for analysing economic and financial data. In our case, wavelets are useful for drawing conclusions both in the time and frequency domains and for obtaining information on the different phases through which the study variables progress.

This research was concerned with the identification of rational models in stochastic processes and we use the Padé-Laurent Approximation to identify a Transfer-Function Model with Expectations. We mention the T-table method and focus in studying the generalised epsilon-algorithm, emphasizing the main role of the statistical significance of their numerical entries. Empirical work is made for non-causal TF models using both simulated and economic real data.