The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Olivier Scaillet - One of the best experts on this subject based on the ideXlab platform.
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indirect inference Nuisance Parameter and threshold moving average models
Journal of Business & Economic Statistics, 2003Co-Authors: Alain Guay, Olivier ScailletAbstract:We analyze the modifications that occur in indirect inference when a Nuisance Parameter is not identified under the null hypothesis. We develop a testing procedure adapted to this simulation-based estimation method, and detail its use for detecting the threshold effect in threshold moving average models with contemporaneous and lagged asymmetries. In contrast to existing threshold models, these models allow taking into account the presence of asymmetric effects of current and lagged random shocks. We use them to measure the persistence of shocks to U.S. output.
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indirect inference Nuisance Parameter and threshold moving average
Research Papers in Economics, 1999Co-Authors: Alain Guay, Olivier ScailletAbstract:We analyse the modifications that occur in indirect inference when a Nuisance Parameter is not identified under the null hypothesis. We develop a testing procedure adapted to this simulation based estimation method, and detail its use for detecting the threshold effect in threshold moving average models with contemporaneous and lagged asymetries. In contrast to existing threshold models, these models allow to take into account the presence of asymetric effects of current and lagged random shocks on US GNP growth rates. Nous analysons les modifications a apporter a la methode d'inference indirecte lorsqu'un parametre de Nuisance n'est pas identifie sous l'hypothese nulle. Nous developpons une procedure de test adaptee a cette methode d'estimation fondee sur des simulations, et detaillons son utilisation dans la detection de l'effet de seuil dans des modeles moyennes mobiles a seuils avec asymetries contemporaines et retardees. Par rapport aux autres modeles a seuils existants, ces modeles permettent de prendre en compte la presence d'effets asymetriques des chocs courants et retardes sur la serie de taux de croissance du PNB americain.
Mark J. Van Der Laan - One of the best experts on this subject based on the ideXlab platform.
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a semiparametric model selection criterion with applications to the marginal structural model
Computational Statistics & Data Analysis, 2006Co-Authors: Alan M Brookhart, Mark J. Van Der LaanAbstract:Estimators of the Parameter of interest in semiparametric models often depend on a guessed model for the Nuisance Parameter. The choice of the model for the Nuisance Parameter can affect both the finite sample bias and efficiency of the resulting estimator of the Parameter of interest. In this paper we propose a finite sample criterion based on cross validation that can be used to select a Nuisance Parameter model from a list of candidate models. We show that expected value of this criterion is minimized by the Nuisance Parameter model that yields the estimator of the Parameter of interest with the smallest mean-squared error relative to the expected value of an initial consistent reference estimator. In a simulation study, we examine the performance of this criterion for selecting a model for a treatment mechanism in a marginal structural model (MSM) of point treatment data. For situations where all possible models cannot be evaluated, we outline a forward/backward model selection algorithm based on the cross validation criterion proposed in this paper and show how it can be used to select models for multiple Nuisance Parameters. Finally, we apply the forward model selection algorithm to a MSM analysis of the relationship between boiled water use and gastrointestinal illness in HIV positive men.
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a semiparametric model selection criterion with applications to the marginal structural model
Computational Statistics & Data Analysis, 2006Co-Authors: Alan M Brookhart, Mark J. Van Der LaanAbstract:Estimators of the Parameter of interest in semiparametric models often depend on a guessed model for the Nuisance Parameter. The choice of the model for the Nuisance Parameter can affect both the finite sample bias and efficiency of the resulting estimator of the Parameter of interest. In this paper we propose a finite sample criterion based on cross validation that can be used to select a Nuisance Parameter model from a list of candidate models. We show that expected value of this criterion is minimized by the Nuisance Parameter model that yields the estimator of the Parameter of interest with the smallest mean-squared error relative to the expected value of an initial consistent reference estimator. In a simulation study, we examine the performance of this criterion for selecting a model for a treatment mechanism in a marginal structural model (MSM) of point treatment data. For situations where all possible models cannot be evaluated, we outline a forward/backward model selection algorithm based on the cross validation criterion proposed in this paper and show how it can be used to select models for multiple Nuisance Parameters. Finally, we apply the forward model selection algorithm to a MSM analysis of the relationship between boiled water use and gastrointestinal illness in HIV positive men.
Jonathan B Hill - One of the best experts on this subject based on the ideXlab platform.
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inference when there is a Nuisance Parameter under the alternative and some Parameters are possibly weakly identified
Social Science Research Network, 2018Co-Authors: Jonathan B HillAbstract:We present a new robust bootstrap method for a test when there is a Nuisance Parameter under the alternative, and some Parameters are possibly weakly or non-identified. We focus on a Bierens (1990)-type conditional moment test of omitted nonlinearity for convenience, and because of difficulties that have been ignored to date. Existing methods include the supremum p-value which promotes a conservative test that is generally not consistent, and test statistic transforms like the supremum and average for which bootstrap methods are not valid under weak identification. We propose a new wild bootstrap method for p-value computation by targeting specific identification cases. We then combine bootstrapped p-values across polar identification cases to form an asymptotically valid p-value approximation that is robust to any identification case. The wild bootstrap does not require knowledge of the covariance structure of the bootstrapped processes, whereas Andrews and Cheng's (2012, 2013, 2014) simulation approach generally does. Our method allows for robust bootstrap critical value computation as well. Our bootstrap method (like conventional ones) does not lead to a consistent p-value approximation for test statistic functions like the supremum and average. We therefore smooth over the robust bootstrapped p-value as the basis for several tests which achieve the correct asymptotic level, and are consistent, for any degree of identification. A simulation study reveals possibly large empirical size distortions in non-robust tests when weak or non-identification arises. One of our smoothed p-value tests, however, dominates all other tests by delivering accurate empirical size and comparatively high power.
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a smoothed p value test when there is a Nuisance Parameter under the alternative
arXiv: Methodology, 2015Co-Authors: Jonathan B HillAbstract:We present a new test when there is a Nuisance Parameter under the alternative hypothesis. The test exploits the p-value occupation time [PVOT], the measure of the subset of on which a p-value test based on a test statisticTn( ) rejects the null hypothesis. The PVOT has only been explored in Hill and Aguilar (2013) and Hill (2012) as a way to smooth over a trimming Parameter for heavy tail robust test statistics. Our key contributions are: (i) we show that a weighted average local power of a test based onTn( ) is identically a weighted average mean PVOT, and the PVOT used for our test is therefore a point estimate of the weighted average probability of PV test rejection, under the null; (ii) an asymptotic critical value upper bound for our test is the signicance level itself, making inference
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a smoothed p value test when there is a Nuisance Parameter under the alternative
Social Science Research Network, 2015Co-Authors: Jonathan B HillAbstract:We present a new test when there is a Nuisance Parameter λ under the alternative hypothesis. The test exploits the p-value occupation time [PVOT], the measure of the subset of λ on which a p-value test based on a test statistic Tn(λ) rejects the null hypothesis. The PVOT has only been explored in Hill and Aguilar (2013) and Hill (2012) as a way to smooth over a trimming Parameter for heavy tail robust test statistics. Our key contributions are: (i) we show that a weighted average local power of a test based on Tn(λ) is identically a weighted average mean PVOT, and the PVOT used for our test is therefore a point estimate of the weighted average probability of PV test rejection, under the null; (ii) an asymptotic critical value upper bound for our test is the significance level itself, making inference easy (as opposed to supremum and average test statistic transforms which typically require a bootstrap method for p-value computation); (iii) we only require Tn(λ) to have a known or bootstrappable limit distribution, hence we do not require √n-Gaussian asymptotics as is nearly always assumed, and we allow for some Parameters to be weakly or non-identified; and (iv) a numerical experiment, in which local asymptotic power is computed for a test of omitted nonlinearity, reveals the asymptotic critical value is exactly the significance level, and the PVOT test is virtually equivalent to a test with the greatest weighted average power in the sense of Andrews and Ploberger (1994)We give examples of PVOT tests of omitted nonlinearity, GARCH effects and a one time structural break. A simulation study demonstrates the merits of PVOT test of omitted nonlinearity and GARCH effects, and demonstrates the asymptotic critical value is exactly the significance level.
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asymptotically Nuisance Parameter free consistent tests of functional form
2006Co-Authors: Jonathan B HillAbstract:We develop a consistent conditional moment test of-best predictor functional form,1· 2. Our main result is a reduction of the Nuisance Parameter space to the set of integers which greatly simpli…es asymptotic theory, and allows for removal of the Nuisance Parameter in a mechanical fashion. Our results provide a fresh vantage into why Bierens’ (1990) moment condition works, and uncovers a new class of weights which sharply contrasts with Stinchcombe and White’s (1997) weight classi…cation (real analytic and non-polynomial). The computation of a weighted-Average CM statistic is easy and asymptotically Nuisance Parameter free because it incorporates all possible Nuisance Parameter values. Our test serves as a consistent model check in-regression environments. Finally, we provide a simple Nuisance Parameter free series expansion of the best-predictor.
Werner Ploberger - One of the best experts on this subject based on the ideXlab platform.
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admissibility of the likelihood ratio test when a Nuisance Parameter is present only under the alternative
Annals of Statistics, 1995Co-Authors: Donald W K Andrews, Werner PlobergerAbstract:This paper establishes the asymptotic admissibility of the likelihood ratio (LR) test for a general class of testing problems in which a Nuisance Parameter is present only under the alternative hypothesis. The paper also establishes the finite sample admissibility of the LR test for testing problems of this sort that arise in Gaussian linear regression models with known variance.
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optimal tests when a Nuisance Parameter is present only under the alternative
Econometrica, 1994Co-Authors: Donald W K Andrews, Werner PlobergerAbstract:This paper derives asymptotically optimal tests for testing problems in which a Nuisance Parameter exists under the alternative hypothesis but not under the null. For example, the results apply to tests of structural change with unknown changepoint. The testing problem considered is nonstandard and the classical asymptotic optimality results for the Lagrange multiplier, Wald, and likelihood ratio do not apply. A weighted average power criterion is used here to generate optimal tests. This criterion is similar to that used by A. Wald (1943) to obtain the classical asymptotic optimality properties of Wald tests in 'regular' testing problems. Copyright 1994 by The Econometric Society.
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admissibility of the likelihood ratio test when a Nuisance Parameter is present onlyunder the alternative
Research Papers in Economics, 1993Co-Authors: Donald W K Andrews, Werner PlobergerAbstract:This paper establishes the asymptotic admissibility of the likelihood ratio (LR) test for a general class of testing problems in which a Nuisance Parameter is present only under the alternative hypothesis. The paper also establishes the finite sample admissibility of the LR test for testing problems of this sort that arise in Gaussian linear regression models with known variance.
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optimal tests when a Nuisance Parameter is present only under the alternative
1992Co-Authors: Donald W K Andrews, Werner PlobergerAbstract:This paper derives asymptotically optimal tests for testing problems in which a Nuisance Parameter exists under the alternative hypothesis but not under the null. The results of the paper are of interest, because the testing problem considered in non-standard and the classical asymptotic optimality results for the Wald, Lagrange multiplier (LM), and likelihood ratio (LR) tests do not apply. In the non-standard cases of main interest, new optimal tests are obtained and the LR test is not found to be an optimal test.
Alain Guay - One of the best experts on this subject based on the ideXlab platform.
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indirect inference Nuisance Parameter and threshold moving average models
Journal of Business & Economic Statistics, 2003Co-Authors: Alain Guay, Olivier ScailletAbstract:We analyze the modifications that occur in indirect inference when a Nuisance Parameter is not identified under the null hypothesis. We develop a testing procedure adapted to this simulation-based estimation method, and detail its use for detecting the threshold effect in threshold moving average models with contemporaneous and lagged asymmetries. In contrast to existing threshold models, these models allow taking into account the presence of asymmetric effects of current and lagged random shocks. We use them to measure the persistence of shocks to U.S. output.
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indirect inference Nuisance Parameter and threshold moving average
Research Papers in Economics, 1999Co-Authors: Alain Guay, Olivier ScailletAbstract:We analyse the modifications that occur in indirect inference when a Nuisance Parameter is not identified under the null hypothesis. We develop a testing procedure adapted to this simulation based estimation method, and detail its use for detecting the threshold effect in threshold moving average models with contemporaneous and lagged asymetries. In contrast to existing threshold models, these models allow to take into account the presence of asymetric effects of current and lagged random shocks on US GNP growth rates. Nous analysons les modifications a apporter a la methode d'inference indirecte lorsqu'un parametre de Nuisance n'est pas identifie sous l'hypothese nulle. Nous developpons une procedure de test adaptee a cette methode d'estimation fondee sur des simulations, et detaillons son utilisation dans la detection de l'effet de seuil dans des modeles moyennes mobiles a seuils avec asymetries contemporaines et retardees. Par rapport aux autres modeles a seuils existants, ces modeles permettent de prendre en compte la presence d'effets asymetriques des chocs courants et retardes sur la serie de taux de croissance du PNB americain.
