Improving the Estimation and Predictions of Small Time Series Models



Liu-Evans, Gareth ORCID: 0000-0002-5880-2781
(2023) Improving the Estimation and Predictions of Small Time Series Models. Journal of Time Series Econometrics, 15 (1). pp. 1-26.

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Abstract

<jats:title>Abstract</jats:title> <jats:p>A new approach is developed for improving the point estimation and predictions of parametric time-series models. The method targets performance criteria such as estimation bias, root mean squared error, variance, or prediction error, and produces closed-form estimators 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 autoregressive (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.</jats:p>

Item Type: Article
Divisions: Faculty of Humanities and Social Sciences > School of Management
Depositing User: Symplectic Admin
Date Deposited: 14 Mar 2022 16:55
Last Modified: 13 Apr 2023 01:30
DOI: 10.1515/jtse-2021-0051
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3150783