Forecasting autoregressive time series with bias-corrected parameter estimators

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Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals (2011, methodology): “6 See Kim (2003) for details of bias-corrected point forecasting based on the bootstrap. where the model is selected from all models with either multiplicative or additive errors, and (vi) ETS2, where the model is selected from those with additive errors only.”
25 years of time series forecasting (2006, background): “More recently, Kim (2003) considered parameter estimation and forecasting of AR models in small samples.”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “Kim (2003) considered parameter estimation and forecasting of AR models in small samples. He found that bias-corrected parameter estimators produce more accurate forecasts than the least squares estimator. Landsman & Damodaran (1989) presented evidence that the James-Stein ARIMA parameter estimator ”