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Improving the Estimation and Predictions of Small Time Series Models

Gareth Liu-Evans

No 202106, Working Papers from University of Liverpool, Department of Economics

Abstract: 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
Date: 2021-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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