A Goodness-of-fit Test for Copulas

Huang, Wanling; Prokhorov, Artem

doi:10.1080/07474938.2012.690692

Cited by 49 publications

(44 citation statements)

References 39 publications

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“…Other types of specification tests include Panchenko's (2005) V-statistic type test, Prokhorov and Schmidt's (2009) conditional moment based test, Mesfioui et al's (2009) Spearman dependence based test, and Genest et al's (2011) Pickands dependence based test. Very recently, Huang and Prokhorov (2013) adopted White's test based on information matrix (White, 1982) to derive a test for copula models specification. With the utility of either Kendall's or Rosenblat's probability integral transformations, several other versions of specification tests have been proposed in the literature, including those proposed by Breymann et al (2003), Dobrić and Schmid (2007) and Genest and Favre (2007), among others.…”

Section: Introductionmentioning

confidence: 99%

Goodness-of-fit test for specification of semiparametric copula dependence models

Zhang

Okhrin

Zhou

et al. 2016

Journal of Econometrics

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This paper concerns goodness-of-fit test for semiparametric copula models. Our contribution is two-fold:we first propose a new test constructed via the comparison between "in-sample" and "out-of-sample" pseudolikelihoods, which avoids the use of any probability integral transformations. Under the null hypothesis that the copula model is correctly specified, we show that the proposed test statistic converges in probability to a constant equal to the dimension of the parameter space and establish the asymptotic normality for the test. Second, we introduce a hybrid mechanism to combine several test statistics, so that the resulting test will make a desirable test power among the involved tests. This hybrid method is particularly appealing when there exists no single dominant optimal test. We conduct comprehensive simulation experiments to compare the proposed new test and hybrid approach with the best "blank test" shown in Genest et al. JEL classification: C12; C22; C32; C52; G15.KEY WORDS: hybrid test; in-and-out-of sample

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Section: Introductionmentioning

confidence: 99%

Goodness-of-fit test for specification of semiparametric copula dependence models

Zhang

Okhrin

Zhou

et al. 2016

Journal of Econometrics

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“…The S B ð Þ n has practical limitations for the Student's t family. Hence, we use the White statistic for the Student's t copula (Huang and Prokhorov 2014). The p values are obtained for the tests using 100 bootstraps.…”

Section: Copula Estimation In Mcqmsmentioning

confidence: 99%

Multivariate copula quantile mapping for bias correction of reanalysis air temperature data

Alidoost

Stein

et al. 2019

Journal of Spatial Science

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Reanalysis data retrieved from the European Centre for Medium-range Weather Forecasts (ECMWF) are commonly used for hydrological studies. Their use requires bias correction, defined as the difference between reanalysis values and measurements. We propose three multivariate copula quantile mappings (MCQMs) to predict bias-corrected values at unvisited locations. We apply the methods to the Qazvin Plain, Iran, for daily air temperature retrieved from weather stations and the ECMWF archive. Results showed that MCQMs reduced bias by 46% as compared with classical quantile mapping. The study concludes that MCQMs are well able to represent the spatial and temporal variation of air temperature.

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“…These results are then used to derive robust hypothesis testing methods for possibly misspecified models in the presence of ignorable or nonignorable missing-data mechanisms. Second, a method for the detection of model misspecification in missing data problems is discussed using recently developed Generalized Information Matrix Tests (GIMT) (Golden et al 2013(Golden et al , 2016; also see Cho and White 2014;Cho and Phillips 2018;Huang and Prokhorov 2014;Ibragimov and Prokhorov 2017;Prokhorov et al 2019;Schepsmeier 2015Schepsmeier , 2016Zhu 2017). Third, we provide regularity conditions for the Missing Information Principle (MIP) to hold in the presence of model misspecification in order to provide useful computational covariance matrix estimation formulas.…”

Section: A Framework For Understanding Misspecification In Missing Damentioning

confidence: 99%

Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data

et al. 2019

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Researchers are often faced with the challenge of developing statistical models with incomplete data. Exacerbating this situation is the possibility that either the researcher’s complete-data model or the model of the missing-data mechanism is misspecified. In this article, we create a formal theoretical framework for developing statistical models and detecting model misspecification in the presence of incomplete data where maximum likelihood estimates are obtained by maximizing the observable-data likelihood function when the missing-data mechanism is assumed ignorable. First, we provide sufficient regularity conditions on the researcher’s complete-data model to characterize the asymptotic behavior of maximum likelihood estimates in the simultaneous presence of both missing data and model misspecification. These results are then used to derive robust hypothesis testing methods for possibly misspecified models in the presence of Missing at Random (MAR) or Missing Not at Random (MNAR) missing data. Second, we introduce a method for the detection of model misspecification in missing data problems using recently developed Generalized Information Matrix Tests (GIMT). Third, we identify regularity conditions for the Missing Information Principle (MIP) to hold in the presence of model misspecification so as to provide useful computational covariance matrix estimation formulas. Fourth, we provide regularity conditions that ensure the observable-data expected negative log-likelihood function is convex in the presence of partially observable data when the amount of missingness is sufficiently small and the complete-data likelihood is convex. Fifth, we show that when the researcher has correctly specified a complete-data model with a convex negative likelihood function and an ignorable missing-data mechanism, then its strict local minimizer is the true parameter value for the complete-data model when the amount of missingness is sufficiently small. Our results thus provide new robust estimation, inference, and specification analysis methods for developing statistical models with incomplete data.

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A Goodness-of-fit Test for Copulas

Cited by 49 publications

References 39 publications

Goodness-of-fit test for specification of semiparametric copula dependence models

Goodness-of-fit test for specification of semiparametric copula dependence models

Multivariate copula quantile mapping for bias correction of reanalysis air temperature data

Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data

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