Estimation and inference for conditionally heteroscedastic models
The ordinary least squares (OLS) method is known to be efficient for linear models when the errors are homogeneous with Gaussian distributions, but troublesome with heteroscedastic or non-Gaussian errors. For the latter nonstandard case, we use the weighted quantile regression (l$\sb1$) method, gaining both robustness and efficiency, with successful applications to interval forecasting of ARCH type time series models.
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign