Tests of linear hypotheses based on regression rank scores

Gütenbrunner, Christoph; Jurečková, Jana; Koenker, Roger; Portnoy, Stephen

doi:10.1080/10485259308832561

Cited by 163 publications

(116 citation statements)

References 24 publications

(30 reference statements)

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“…It is also apparent that the pivotal nature of the finite sample approach is similar to the asymptotically pivotal nature of the rank-score method, cf. Gutenbrunner, Jurečková, Koenker, and Portnoy (1993) and Koenker (1997), and the bootstrap method of Parzen, Wei, and Ying (1994). 1 In sharp contrast to these methods, the finite sample approach does not…”

Section: Model and Sampling Assumptionsmentioning

confidence: 99%

Finite sample inference for quantile regression models

Chernozhukov

Hansen

Jansson

2009

Journal of Econometrics

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Abstract. Under minimal assumptions finite sample confidence bands for quantile regression models can be constructed. These confidence bands are based on the "conditional pivotal property" of estimating equations that quantile regression methods aim to solve and will provide valid finite sample inference for both linear and nonlinear quantile models regardless of whether the covariates are endogenous or exogenous. The confidence regions can be computed using MCMC, and confidence bounds for single parameters of interest can be computed through a simple combination of optimization and search algorithms. We illustrate the finite sample procedure through a brief simulation study and two empirical examples: estimating a heterogeneous demand elasticity and estimating heterogeneous returns to schooling. In all cases, we find pronounced differences between confidence regions formed using the usual asymptotics and confidence regions formed using the finite sample procedure in cases where the usual asymptotics are suspect, such as inference about tail quantiles or inference when identification is partial or weak. The evidence strongly suggests that the finite sample methods may usefully complement existing inference methods for quantile regression when the standard assumptions fail or are suspect.

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Section: Model and Sampling Assumptionsmentioning

confidence: 99%

Finite sample inference for quantile regression models

Chernozhukov

Hansen

Jansson

2009

Journal of Econometrics

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“…This led us to also consider unmodified versions of robust M-tests (type e and type t tests) proposed by Thompson (2001). Thompson's type t test is analogous to the test suggested by Gutenbrunner et al (1993). Indeed, the unmodified type t test has proven to work quite well in the presence of inliers in the simulations in this paper, while the Hasan and Koenker (1997) test, a modified version of the t test, does not work well in this case.…”

Section: Unit Root Testsmentioning

confidence: 90%

Inflation inertia and inliers: the case of Brazil

Campelo

Cribari‐Neto

2003

Rev. Bras. Econ.

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Summary: 1. The issue; 2. The data; 3. Unit root tests; 4. Measuring inflation inertia; 5. Some simulation results; 6. Concluding remarks and discussion.Keywords: inflation inertia; inliers; persistence; unit roots. JEL codes: C22; C12; C15. This paper addresses the issue of measuring the degree of inertia in inflation in the presence of potential 'inliers'. It shows that by using robust unit root tests one reaches the same inference on the order of integration of the series as what is revealed by the modified procedure proposed by Cati et al. (1999). The results also suggest that, contrary to previous findings, the degree of inertia in inflation is rather small. Finally, the paper presents simulation results on the finite-sample behavior of unit root tests and of a persistence measure when the data contain inliers.Este artigo analisa a mensuração do grau de inércia na inflação na presença de potenciais inliers. O artigo mostra que testes robustos de raízes unitárias conduzemà mesma inferência sobre a ordem de integração da série do que o procedimento modificado proposto por Cati et al. (1999). Os resultados sugerem que o grau de inércia na inflação brasileiraé pequeno. Por fim, o artigo apresenta resultados de simulação de Monte Carlo sobre o desempenho em amostras finitas de testes de raízes unitárias e de uma medida de persistência quando os dados contêm inliers.

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“…The asymptotic distribution of regression quantiles and of regression rank scores under n → ∞ was studied by more authors, under various conditions on f and on the regressors [let us mention Koenker and Bassett (1978), Ruppert and Carroll (1980), Gutenbrunner and Jurečková (1992), Gutenbrunner et al (1993), among others]. The asymptotic properties of autoregression quantiles and the rank scores was studied by Koul and Saleh (1995).…”

Section: Introductionmentioning

confidence: 99%

“…e.g. Gutenbrunner and Jurečková (1992), Gutenbrunner et al (1993)). Moreover, the asymptotic distribution, which is typically normal, does not provide the full information on the behavior ofβ(α), and it can stretch its true behavior under heavy-tailed f.…”

Section: Introductionmentioning

confidence: 99%

Finite-sample distribution of regression quantiles

Jurečková

2010

Statistics & Probability Letters

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The finite sample distributions of the regression quantile and of the extreme regression quantile are derived for a broad class of distributions of the model errors, even for the non-i.i.d case. The distributions are analogous to the corresponding distributions in the location model; this again confirms that the regression quantile is a straightforward extension of the sample quantile. As an application, the tail behavior of the regression quantile is studied.

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Tests of linear hypotheses based on regression rank scores

Cited by 163 publications

References 24 publications

Finite sample inference for quantile regression models

Finite sample inference for quantile regression models

Inflation inertia and inliers: the case of Brazil

Finite-sample distribution of regression quantiles

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