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Full-Text Articles in Applied Statistics
Adaptive Estimation, Douglas G. Steigerwald
Adaptive Estimation, Douglas G. Steigerwald
Douglas G. Steigerwald
No abstract provided.
Uniformly Adaptive Estimation For Models With Arma Errors, Douglas Steigerwald
Uniformly Adaptive Estimation For Models With Arma Errors, Douglas Steigerwald
Douglas G. Steigerwald
A semiparametric estimator based on an unknown density is uniformly adaptive if the expected loss of the estimator converges to the asymptotic expected loss of the maximum likelihood estimator based on the true density (MLE), and if convergence does not depend on either the parameter values or the form of the unknown density. Without uniform adaptivity, the asymptotic expected loss of the MLE need not approximate the expected loss of a semiparamteric estimator for any finite sample. I show that a two-step semiparametric estimator is uniformly adaptive for the parameters of nonlinear regression models with autoregressive moving average errors.
Adaptive Estimation In Timeseries Regression Models, Douglas Steigerwald
Adaptive Estimation In Timeseries Regression Models, Douglas Steigerwald
Douglas G. Steigerwald
I develop adaptive estimators for linear regression with serially correlated errors. The efficiency results hold even when the serial correlation structure is unknown. Simulations indicate that efficiency gains can be substantial with samples of only 50 observations. We apply the method to a study of forward exchange rates.
On The Finite Sample Behavior Of Adaptive Estimators, Douglas Steigerwald
On The Finite Sample Behavior Of Adaptive Estimators, Douglas Steigerwald
Douglas G. Steigerwald
With only 50 observations, the adaptive estimator produces confidence intervals that are 20 to 50 percent shorter than those produced by GLS procedures. The key feature is that the underlying error density is symmetric. Under asymmetry the interval length is shortened by a smaller amount.