What question did this study set out to answer?

To introduce a goodness-of-fit process for quantile regression analogous to the R-squared statistic in least squares regression and formulate related inference processes.

December 1, 1999Journal of the American Statistical Association

Goodness of Fit and Related Inference Processes for Quantile Regression

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To introduce a goodness-of-fit process for quantile regression analogous to the R-squared statistic in least squares regression and formulate related inference processes.
Formulated inference processes to test composite hypotheses regarding the combined impact of multiple covariates across an entire spectrum of conditional quantile functions.
Characterized the asymptotic behavior of the proposed inference processes by linking them to p-sample goodness-of-fit theory based on Bessel processes.
Validated the testing methodology using hypothetical examples, empirical applications to international economic growth models, and Monte Carlo experiments.
The asymptotic distribution of the quantile regression goodness-of-fit process was formally derived and linked to classical Bessel processes.
Monte Carlo evidence and empirical growth model applications confirmed the effectiveness of the process for evaluating joint covariate effects across multiple quantiles.

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Implication

Methodological study demonstrates goodness-of-fit processes for quantile regression across conditional quantile functions, highlighting improved hypothesis testing using Bessel processes.

Cite This Study

Koenker et al. (1999) studied this question.

synapsesocial.com/papers/69dc7fb724e766dc313594b8 https://doi.org/10.1080/01621459.1999.10473882

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Implication

Methodological study demonstrates goodness-of-fit processes for quantile regression across conditional quantile functions, highlighting improved hypothesis testing using Bessel processes.

Goodness of Fit and Related Inference Processes for Quantile Regression

Key Points

Discussion

Implication

Cite This Study

Goodness of Fit and Related Inference Processes for Quantile Regression

Key Points

Discussion

Implication

Cite This Study

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