Improving the Estimation and Predictions of Small Time Series Models
No 202106, Working Papers from University of Liverpool, Department of Economics
A new approach is developed for improving the point estimation and predictions of para-metric time-series models. The method targets performance criteria such as estimation bias, root mean squared error, variance, or prediction error, and produces closed-form es-timators focused towards these targets via a computational approximation method. This is done for an autoregression coefficient, for the mean reversion parameter in Vasicek and CIR diffusion models, for the Binomial thinning parameter in integer-valued autoregres-sive (INAR) models, and for predictions from a CIR model. The success of the prediction targeting approach is shown in Monte Carlo simulations and in out-of-sample forecasting of the US Federal Funds rate.
Pages: 31 pages
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Persistent link: https://EconPapers.repec.org/RePEc:liv:livedp:202106
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