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Improving the Estimation and Predictions of Small Time Series Models

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  • Liu-Evans Gareth

    (Management School, University of Liverpool, Chatham Street, Liverpool, L69 7ZH, UK)

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.

Suggested Citation

  • Liu-Evans Gareth, 2023. "Improving the Estimation and Predictions of Small Time Series Models," Journal of Time Series Econometrics, De Gruyter, vol. 15(1), pages 1-26, January.
    • Handle: RePEc:bpj:jtsmet:v:15:y:2023:i:1:p:1-26:n:3
    DOI: 10.1515/jtse-2021-0051
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    More about this item

    Keywords

    Bias correction; Forecasting; likelihood-free estimation; Time series; Diffusions; Count data;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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