An Innovation Approach to Goodness-of-Fit Tests in $R^m$

Khmaladze, Estáte V.

doi:10.1214/aos/1176351051

Cited by 72 publications

(39 citation statements)

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“…Their focus is the specification of the conditional mean, rather than the conditional distribution. Incidentally, extension of the transformation to AR(2) or multiple-regressor marked-empirical processes is non-trivial because, among other technical difficulties, Khmaladze's transformation is not unique for multivariate empirical processes, see Khmaladze (1988Khmaladze ( , 1993. A marked empirical process for high dimensional models is a multivariate process (in-dexed by a vector).…”

Section: Related Literature and Contributions Of This Papermentioning

confidence: 99%

Testing Parametric Conditional Distributions of Dynamic Models

Bai

2003

Review of Economics and Statistics

209

262

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This paper proposes a nonparametric test for parametric conditional distributions of dynamic models. The test is of the Kolmogorov type coupled with Khmaladze's martingale transformation. The proposed test is asymptotically distribution free and has non-trivial power against root-n local alternatives. The method is applicable for various dynamic models, including autoregressive and moving average models (ARMA), generalized autoregressive conditional heteroskedasticity (GARCH), integrated GARCH (IGARCH), and general nonlinear time series regressions. The method is also applicable for cross sectional models. Finally, we apply the procedure to testing conditional normality and conditional t-distribution in a GARCH model for the NYSE equal-weighted returns.

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Section: Related Literature and Contributions Of This Papermentioning

confidence: 99%

Testing Parametric Conditional Distributions of Dynamic Models

Bai

2003

Review of Economics and Statistics

209

262

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“…The following lemma, together its proof, is similar to the one from Proposition 4.1 of Khmaladze and Koul (2004) and Lemma 9.1 of Koul (2006), which in turn has origin in Khmaladze (1988). For the sake of brevity, the proof is omitted here.…”

Section: Proofsmentioning

confidence: 77%

A lack-of-fit test in Tobit errors-in-variables regression models

Song

Yao

2011

Statistics & Probability Letters

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“…The results are explicitly used in e.g. Khmaladze (1988) We conclude that Oosterhoff and Van Zwet (1979) is a fundamental paper, which presents useful tools for verifying contiguity.…”

Section: A Note On Contiguity and Hellinger Distancementioning

confidence: 99%