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Featured researches published by Qiying Wang.


Econometric Theory | 2009

ASYMPTOTIC THEORY FOR LOCAL TIME DENSITY ESTIMATION AND NONPARAMETRIC COINTEGRATING REGRESSION

Qiying Wang; Peter C. B. Phillips

We provide a new asymptotic theory for local time density estimation for a general class of functionals of integrated time series. This result provides a convenient basis for developing an asymptotic theory for nonparametric cointegrating regression and autoregression. Our treatment directly involves the density function of the processes under consideration and avoids Fourier integral representations and Markov process theory which have been used in earlier research on this type of problem. The approach provides results of wide applicability to important practical cases and involves rather simple derivations that should make the limit theory more accessible and useable in econometric applications. Our main result is applied to offer an alternative development of the asymptotic theory for non-parametric estimation of a non-linear cointegrating regression involving non-stationary time series. In place of the framework of null recurrent Markov chains as developed in recent work of Karlsen, Myklebust and Tjostheim (2007), the direct local time density argument used here more closely resembles conventional nonparametric arguments, making the conditions simpler and more easily verified.


Econometrica | 2008

Structural Nonparametric Cointegrating Regression

Qiying Wang; Peter C. B. Phillips

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill-posed inverse problems. In functional cointegrating regressions where the regressor is an integrated or near-integrated time series, it is shown here that inverse and ill-posed inverse problems do not arise. Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics that are useable in practical work. It is further shown that use of augmented regression, as is common in linear cointegration modeling to address endogeneity, does not lead to bias reduction in nonparametric regression, but there is an asymptotic gain in variance reduction. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near-integrated regressor. The methods may be applied to a range of empirical models where functional estimation of cointegrating relations is required.


Econometric Theory | 2011

ASYMPTOTIC THEORY FOR ZERO ENERGY FUNCTIONALS WITH NONPARAMETRIC REGRESSION APPLICATIONS

Qiying Wang; Peter C. B. Phillips

A local limit theorem is given for the sample mean of a zero energy function of a nonstationary time series involving twin numerical sequences that pass to infinity. The result is applicable in certain nonparametric kernel density estimation and regression problems where the relevant quantities are functions of both sample size and bandwidth. An interesting outcome of the theory in nonparametric regression is that the linear term is eliminated from the asymptotic bias. In consequence and in contrast to the stationary case, the Nadaraya–Watson estimator has the same limit distribution (to the second order including bias) as the local linear nonparametric estimator.


Journal of Theoretical Probability | 2003

Strong approximation for long memory processes with applications

Qiying Wang; Yan-Xia Lin; Chandra Gulati

In this paper we inverstigate the strong approximation of a linear process with long memory to a Gaussian process. The results are then applied to derive the law of the iterated logarithm and Darling–Erdős type theorem for long memory processes under ideal conditions.


Annals of Statistics | 2012

A specification test for nonlinear nonstationary models

Qiying Wang; Peter C. B. Phillips

We provide a limit theory for a general class of kernel smoothed U-statistics that may be used for specification testing in time series regression with nonstationary data. The test framework allows for linear and nonlinear models with endogenous regressors that have autoregressive unit roots or near unit roots. The limit theory for the specification test depends on the self-intersection local time of a Gaussian process. A new weak convergence result is developed for certain partial sums of functions involving nonstationary time series that converges to the intersection local time process. This result is of independent interest and is useful in other applications. Simulations examine the finite sample performance of the test.


Econometric Theory | 2003

Asymptotics for general fractionally integrated processes with applications to unit root tests

Qiying Wang; Yang Xia Lin; Chandra Gulati

In this paper, functional limit theorems for general fractional processes are established under quite weak conditions. The results are then used to derive weak convergence of general nonstationary fractionally integrated processes and to characterize unit root distribution in a model with error being a fractional autoregressive moving average process or a nonstationary fractionally integrated process. The authors thank three referees and an associate editor for their detailed reading of this paper and valuable comments, which have led to this much improved version of the paper.


Econometric Theory | 2013

NONPARAMETRIC COINTEGRATING REGRESSION WITH NNH ERRORS

Qiying Wang; Ying Xiang Rachel Wang

This paper studies a nonlinear cointegrating regression model with nonlinear nonstationary heteroskedastic error processes. We establish uniform consistency for the conventional kernel estimate of the unknown regression function and develop atwo-stage approach for the estimation of the heterogeneity generating function.


Econometric Theory | 2002

The invariance principle for linear processes with applications

Qiying Wang; Yan-Xia Lin; Chandra Gulati

Let Xt be a linear process defined by Xt 5 (k50 ‘ cket2k, where


Annals of Probability | 2004

Exact convergence rate and leading term in central limit theorem for student's t statistic

Peter Hall; Qiying Wang

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Probability Surveys | 2013

Self-normalized limit theorems: A survey

Qi-Man Shao; Qiying Wang

0% is a sequence of real numbers and

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Peter C. B. Phillips

Singapore Management University

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Bing-Yi Jing

Hong Kong University of Science and Technology

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Chandra Gulati

University of Wollongong

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Qi-Man Shao

Hong Kong University of Science and Technology

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Zhishui Hu

University of Science and Technology of China

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Peter Hall

University of Melbourne

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Yan-Xia Lin

University of Wollongong

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