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Yixiao Sun
Researcher at University of California, San Diego
Publications - 102
Citations - 2421
Yixiao Sun is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Estimator & Heteroscedasticity. The author has an hindex of 26, co-authored 93 publications receiving 2129 citations. Previous affiliations of Yixiao Sun include Wuhan University & Cowles Foundation.
Papers
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Optimal Bandwidth Selection in Heteroskedasticity-Autocorrelation Robust Testing
TL;DR: In this paper, the authors consider studentized tests in time series regressions with nonparametrically autocorrelated errors and show that for typical economic time series, the optimal bandwidth that minimizes a weighted average of type I and type II errors is larger by an order of magnitude than the bandwidth which minimizes the asymptotic mean squared error of the corresponding long-run variance estimator.
Posted Content
Optimal Bandwidth Selection in Heteroskedasticity-Autocorrelation Robust Testing
TL;DR: In this article, the authors show that the nonstandard fixed-b limit distributions of such nonparametrically studentized tests provide more accurate approximations to the finite sample distributions than the standard smallb limit distribution.
Journal ArticleDOI
Adaptive local polynomial whittle estimation of long-range dependence
Donald W.K. Andrews,Yixiao Sun +1 more
TL;DR: In this article, the authors generalize the local Whittle estimator to circumvent the problem of sample bias that can be large and approximate its logarithm by a polynomial.
Journal ArticleDOI
The Tobit Model with a Non-Zero Threshold
Richard T. Carson,Yixiao Sun +1 more
TL;DR: In this article, a new estimator is proposed to estimate the unknown censoring threshold, which is shown to be superconsistent and follows an exponential distribution in large samples, and statistical tests for the estimator are introduced.
Journal ArticleDOI
Understanding the Fisher equation
Yixiao Sun,Peter C.B. Phillips +1 more
TL;DR: In this paper, a bivariate exact Whittle (BEW) estimator is proposed for the presence of short memory noise in the data, which enhances the empirical capacity to separate low-frequency behaviour from high-frequency fluctuations, and produces estimates of long-range dependence that are much less biased when there is noise contaminated data.