Proietti, Tommaso (2008): Direct and iterated multistep AR methods for difference stationary processes.
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The paper focuses on the comparison of the direct and iterated AR predictors for difference stationary processes. In particular, it provides new methods for comparing the efficiency of the two predictors and for extracting the trend from macroeconomic time series using the two methods. The methods are based on an encompassing representation for the two predictors which enables to derive their properties quite easily under a maintained model. The paper provides an analytic expression for the mean square forecast error of the two predictors and derives useful recursive formulae for computing the direct and iterated coefficients. From the empirical standpoint, we propose estimators of the AR coefficients based on the tapered Yule- Walker estimates; we also provide a test of equal forecast accuracy which is very simple to implement and whose critical values are obtained with the bootstrap method.
|Item Type:||MPRA Paper|
|Original Title:||Direct and iterated multistep AR methods for difference stationary processes|
|Keywords:||Multistep estimation; Tapered Yule-Walker estimates; Forecast combination.|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
|Depositing User:||Tommaso Proietti|
|Date Deposited:||25. May 2009 09:42|
|Last Modified:||15. Feb 2013 23:10|
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Direct and iterated multistep AR methods for difference stationary processes. (deposited 01. Oct 2008 08:41)
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