Optimal Autoregressive Predictions
This paper proposes a new, optimal estimator of the AR(1) coefficient that minimixes the prediction mean-squared-error. This estimator can be used to generate an optimal predictor. The new estimator¡®s asymptotic distributions are derived for the cases of stationarity and a near unit root. The optimal estimator is also derived for the AR(p) model (p>=2) and its asymptotic distributions are reported. Simulation results confirm advantages of using the optimal estimator for prediction.
|Date of creation:||Feb 2016|
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- Phillips, Peter C B, 1988.
"Regression Theory for Near-Integrated Time Series,"
Econometric Society, vol. 56(5), pages 1021-1043, September.
- Peter C.B. Phillips, 1986. "Regression Theory for Near-Integrated Time Series," Cowles Foundation Discussion Papers 781R, Cowles Foundation for Research in Economics, Yale University, revised Jan 1987.
- Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
- Choi, In, 1993. "Asymptotic Normality of the Least-Squares Estimates for Higher Order Autoregressive Integrated Processes with Some Applications," Econometric Theory, Cambridge University Press, vol. 9(02), pages 263-282, April. Full references (including those not matched with items on IDEAS)
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