Jiti Gao
Monash University
285 Papers
1.3K Citations
Jiti Gao is an academic researcher from Monash University. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 32, co-authored 270 publications. Previous affiliations of Jiti Gao include Peking University & University of Science and Technology of China.
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Papers
Uniform consistency of nonstationary kernel-weighted sample covariances for nonparametric regression
TL;DR: In this paper, uniform consistency results for kernel-weighted sample covariances in a nonstationary multiple regression framework that allows for both fixed design and random design coefficient variation were obtained.
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Nonparametric simultaneous testing for structural breaks
TL;DR: In this paper, the authors consider a regression model with errors that are martingale differences and develop nonparametric testing procedures that simultaneously test for structural breaks in the conditional mean and the conditional variance.
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A quantile regression approach to panel data analysis of health‐care expenditure in Organisation for Economic Co‐operation and Development countries
Fengping Tian,Jiti Gao,Ke Yang +2 more
TL;DR: It is shown that Baumol's model of "unbalanced growth" has a significantly positive effect on per capita health spending growth, and its effect is quite stable over the entire distribution, while the correlation between the components (wage growth and labor productivity growth) of the "Baumol variable" and health expenditure growth is more varied.
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On income and price elasticities for energy demand: a panel data study
Jiti Gao,Bin Peng,Russell Smyth +2 more
TL;DR: In this paper, the authors proposed an integrated framework to estimate the income and price elasticity of energy demand in 65 countries over the period 1960-2016, and applied it to a large dataset of 65 countries.
24
Asymptotic normality of pseudo-LS estimator for partly linear autoregression models
TL;DR: In this paper, a class of asymptotically normal estimators of β were directly obtained based on piecewise polynomial approximator g T (·) of g and the model Y t = βY t−1 + g t (Y t −2 ) + e t.
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