Jr. Halbert L. White’s research while affiliated with University of Nottingham and other places

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Publications (2)


Estimation, Inference, and Specification Testing for Possibly Misspecified Quantile Regression
  • Article

January 2002

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46 Reads

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77 Citations

SSRN Electronic Journal

Jr. Halbert L. White

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To date the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model is correctly specified. When the model is misspecified, confidence intervals and hypothesis tests based on the conventional covariance matrix are invalid. Although misspecification is a generic phenomenon and correct specification is rare in reality, there has to date been no theory proposed for inference when a conditional quantile model may be misspecified. In this paper, we allow for possible misspecification of a linear conditional quantile regression model. We obtain consistency of the quantile estimator for certain "pseudo-true" parameter values and asymptotic normality of the quantile estimator when the model is misspecified. In this case, the asymptotic covariance matrix has a novel form, not seen in earlier work, and we provide a consistent estimator of the asymptotic covariance matrix. We also propose a quick and simple test for conditional quantile misspecification based on the quantile residuals.


Citations (2)


... The term σ b s,s 2 , on the other hand, is the bootstrap variance estimator, which requires more careful consideration for first order validity of the bootstrap variance estimator (Götze and Künsch, 1996;Goncalves and White, 2004). More specifically, we follow Goncalves and White (2004) and use the following bootstrap variance estimator: ...

Reference:

Testing Quantile Forecast Optimality
Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models
  • Citing Article
  • January 2001

SSRN Electronic Journal

... Nevertheless, previous studies using quantile regressions do not seem to provide consistent estimators. To this end, Parente and Santos Silva (2015) follow the findings of Tae-Hwan Kim and Halbert White (2003) and find evidence that the traditional estimator in quantile regression is consistent and asymptotically normal if there exists within-cluster correlation of the error term and an estimator that is consistent with intra-cluster correlation. Therefore, this technique allows us to run quantile regressions when data are from different individuals as in a panel data set, with the consideration that observations for different groups are independent and intra-cluster correlation may be present (Parente and Santos Silva, 2015). ...

Estimation, Inference, and Specification Testing for Possibly Misspecified Quantile Regression
  • Citing Article
  • January 2002

SSRN Electronic Journal