Abstract
This chapter presents a methodology to fit nonlinear autoregressive moving average polynomial models with exogenous variables (NARMAX) to observed data. Because the models are nonlinear, it is sometimes possible to perform a more accurate analysis than if linear models were used. On the other hand, the model structure, that is, the set of independent variables has to be chosen with great care lest dynamically meaningless models be fitted to the data. An effective algorithm, that can be used to select the “best” regressors from a set of candidates, is reviewed with details and a simple version of its code is provided. The chapter has been designed to serve as a tutorial introduction to modeling and analysis using NARMAX polynomials of which linear and no-input (univariate) models are special cases. The main features of the approach are illustrated using time series of beef-cattle prices for the Brazilian State of So Paulo.
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Aguirre, L.A., Aguirre, A. (2002). Analysis of Economic Time Series Using Narmax Polynomial Models. In: Soofi, A.S., Cao, L. (eds) Modelling and Forecasting Financial Data. Studies in Computational Finance, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0931-8_11
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DOI: https://doi.org/10.1007/978-1-4615-0931-8_11
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