Improving the Estimation and Predictions of Small Time Series Models

Liu-Evans Gareth ()
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Liu-Evans Gareth: Management School, University of Liverpool, Chatham Street, Liverpool, L69 7ZH, UK

Journal of Time Series Econometrics, 2023, vol. 15, issue 1, 1-26

Abstract: 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.

Keywords: Bias correction; Forecasting; likelihood-free estimation; Time series; Diffusions; Count data (search for similar items in EconPapers)
JEL-codes: C13 C15 C22 C53 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/jtse-2021-0051

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