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Keyword: variance estimation;
7 results found.
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Yulia Kotlyarova, Marcia M Schafgans and Victoria Zinde-Walsh
For local and average kernel based estimators, smoothness conditions ensure that the kernel order determines the rate at which the bias of the estimator goes to zero and thus allows the econometrician to control the rate...Read more...
Peter M Robinson and Supachoke Thawornkaiwong
Central limit theorems are developed for instrumental variables estimates of linear and semi-parametric partly linear regression models for spatial data. General forms of spatial dependence and heterogeneity in explanato...Read more...
Peter M Robinson and J Vidal Sanz
In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensional lattice, for d >= 2, the achievement of asymptotic efficiency under Gaussianity, and asymptotic normality more gene...Read more...
Peter M Robinson
Smoothed nonparametric estimates of the spectral density matrix at zero frequency have been widely used in econometric inference, because they can consistently estimate the covariance matrix of a partial sum of a possibl...Read more...
In a number of econometric models, rules of large-sample inference require a consistent estimate of f(0), where f (?) is the spectral density matrix of yt = ut?xt, for covariance stationary vectors ut, xt. Typically yt i...Read more...
Peter M Robinson and Carlos Velasco
We consider statistical inference in the presence of serial dependence. The main focus is on use of statistics that are constructed as if no dependence were believed present, and are asymptotically normal in the presence...Read more...
This paper provides limit theorems for special density matrix estimators and functionals of it for a bivariate co variance stationary process whose spectral density matrix has singularities not only at the origin but pos...Read more...