
Asymptotic distribution for the proportional covariance model
Asymptotic distribution for the proportional covariance model under mult...
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Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties
This work addresses the issue of large covariance matrix estimation in h...
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Heteroskedasticityrobust inference in linear regression models
This paper considers inference in heteroskedastic linear regression mode...
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Bounded support in linear random coefficient models: Identification and variable selection
We consider linear random coefficient regression models, where the regre...
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Asymptotic Degradation of Linear Regression Estimates With Strategic Data Sources
We consider the problem of linear regression from strategic data sources...
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Testing (Infinitely) Many Zero Restrictions
We present a maxtest statistic for testing (possibly infinitely) many z...
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Integral Transform Methods in GoodnessofFit Testing, II: The Wishart Distributions
We initiate the study of goodnessoffit testing when the data consist o...
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Positive definiteness of the asymptotic covariance matrix of OLS estimators in parsimonious regressions
Recently, Ghysels, Hill, and Motegi (2020) proposed a test for examining whether a large number of coefficients in linear regression models is zero. The test is called the max test. The test statistic is calculated by first running multiple ordinary least squares (OLS) regressions, each including only one of key regressors, whose coefficients are supposed to be zero under the null, and then taking the maximum value of the squared OLS coefficient estimates of those key regressors. They called these regressions parsimonious regressions. This paper answers a question raised in their Remark 2.4; whether the asymptotic covariance matrix of the OLS estimators in the parsimonious regressions is generally positive definite. The paper shows that it is generally positive definite, and the result may be utilized to facilitate the calculation of the simulated p value necessary for implementing the max test.
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