{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/21193"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/21193","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Estimation and inference for conditionally heteroscedastic models","abstract":"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.","abstract_html":"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. 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