Many economic and business problems require a set of random variates from the posterior density of the unknown parameters. The set of random variates can be used to integrate numerically many forms of functions. Since a closed form of the posterior density of models in time series analysis is not usually well known, it is not easy to generate a set of random variates. As a sampling scheme based on the probabilities proportional to sizes of the sample space, sampling importance resampling (SIR) method can be applied to generate a set of random variates from the posterior density. Application of SIR to signal extraction model of time series analysis is illustrated and given a set of random variates, the procedures to compute the Monte Carlo estimator of the component of signal extraction model are discussed. The procedures are illustrated with simulated data.
Lee, J.J. (2008), "Applying resampling scheme to time series analysis", Lawrence, K.D. and Geurts, M.D. (Ed.) Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 5), Emerald Group Publishing Limited, Bingley, pp. 267-279. https://doi.org/10.1016/S1477-4070(07)00215-2Download as .RIS
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