Finite-Sample Bias Propagation in the Yule-Walker Method of Autoregressive Estimation
نویسنده
چکیده
Lagged-product autocorrelation estimates have a small triangular bias. However, using them to compute an autoregressive model with the Yule-Walker method can give a strongly distorted spectral model in finite samples. The distortion is shown for examples where the reflection coefficients are not very close to one in absolute value. It will disappear asymptotically. An objective measure is presented to determine the smallest sample size for which the unbiased asymptotic theory is a fair approximation.
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Finite-Sample Bias Propagation in Autoregressive Estimation With the Yule-Walker Method
The Yule–Walker (YW) method for autoregressive (AR) estimation uses lagged-product (LP) autocorrelation estimates to compute an AR parametric spectral model. The LP estimates only have a small triangular bias in the estimated autocorrelation function and are asymptotically unbiased. However, using them in finite samples with the YW method for AR estimation can give a strong distortion in the we...
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