ORCID Profile
0000-0002-9181-3618
Current Organisation
University of Sydney
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Statistics | Probability Theory | Econometric and Statistical Methods | Statistical Theory | Probability Theory | Applied Statistics | Statistical Theory
Expanding Knowledge in Economics | Expanding Knowledge in the Mathematical Sciences | Mathematical sciences |
Publisher: Institute of Mathematical Statistics
Date: 10-2003
Publisher: Cambridge University Press (CUP)
Date: 10-10-2014
DOI: 10.1017/S026646661400070X
Abstract: We examine a kernel regression estimator for time series that takes account of the error correlation structure as proposed by Xiao et al. (2003, Journal of the American Statistical Association 98, 980–992). We show that this method continues to improve estimation in the case where the regressor is a unit root or a near unit root process.
Publisher: Cambridge University Press (CUP)
Date: 18-12-2014
DOI: 10.1017/S0266466614000917
Abstract: This paper explores nonparametric estimation, inference, and specification testing in a nonlinear cointegrating regression model where the structural equation errors are serially dependent and where the regressor is endogenous and may be driven by long memory innovations. Generalizing earlier results of Wang and Phillips (2009a,b, Econometric Theory 25, 710–738, Econometrica 77, 1901–1948), the conventional nonparametric local level kernel estimator is shown to be consistent and asymptotically (mixed) normal in these cases, thereby opening up inference by conventional nonparametric methods to a wide class of potentially nonlinear cointegrated relations. New results on the consistency of parametric estimates in nonlinear cointegrating regressions are provided, extending earlier research on parametric nonlinear regression and providing primitive conditions for parametric model testing. A model specification test is studied and confirmed to provide a valid mechanism for testing parametric specifications that is robust to endogeneity. But under long memory innovations the test is not pivotal, its convergence rate is parameter dependent, and its limit theory involves the local time of fractional Brownian motion. Simulation results show good performance for the nonparametric kernel estimates in cases of strong endogeneity and long memory, whereas the specification test is shown to be sensitive to the presence of long memory innovations, as predicted by asymptotic theory.
Publisher: Elsevier BV
Date: 10-2001
Publisher: Institute of Mathematical Statistics
Date: 2005
DOI: 10.1214/EJP.V10-289
Publisher: Cambridge University Press (CUP)
Date: 29-11-0013
DOI: 10.1017/S026646661300039X
Abstract: For a certain class of martingales, convergence to a mixture of normal distributions is established under convergence in distribution for the conditional variance. This is less restrictive in comparison with the classical martingale limit theorem, where one generally requires convergence in probability. The extension partially removes a barrier in the applications of the classical martingale limit theorem to nonparametric estimation and inference with nonstationarity and enhances the effectiveness of the classical martingale limit theorem as one of the main tools to investigate asymptotics in statistics, econometrics, and other fields. The main result is applied to investigate limit behavior of the conventional kernel estimator in a nonlinear cointegrating regression model, which improves existing works in the literature.
Publisher: SAGE Publications
Date: 22-10-2018
Abstract: The quality of stroke care may diminish on weekends. We aimed to compare the quality of care and outcomes for patients with stroke/transient ischemic attack discharged on weekdays compared with those discharged on weekends. Data from the Australian Stroke Clinical Registry from January 2010 to December 2015 ( n = 45 hospitals) were analyzed. Differences in processes of care by the timing of discharge are described. Multilevel regression and survival analyses (up to 180 days postevent) were undertaken. Among 30,649 registrants, 2621 (8.6%) were discharged on weekends (55% male median age 74 years). Compared to those discharged on weekdays, patients discharged on weekends were more often patients with a transient ischemic attack (weekend 35% vs. 19% p 0.001) but were less often treated in a stroke unit (69% vs. 81% p 0.001), prescribed antihypertensive medication at discharge (65% vs. 71% p 0.001) or received a care plan if discharged to the community (47% vs. 53% p 0.001). After accounting for patient characteristics and clustering by hospital, patients discharged on weekends had a 1 day shorter length of stay (coefficient = −1.31, 95% confidence interval [CI] = −1.52, −1.10), were less often discharged to inpatient rehabilitation (aOR = 0.39, 95% CI = 0.34, 0.44) and had a greater hazard of death within 180 days (hazard ratio = 1.22, 95% CI = 1.04, 1.42) than those discharged on weekdays. Patients with stroke/transient ischemic attack discharged on weekends were more likely to receive suboptimal care and have higher long-term mortality. High quality of stroke care should be consistent irrespective of the timing of hospital discharge.
Publisher: Statistica Sinica (Institute of Statistical Science)
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 18-09-2008
Publisher: Springer Science and Business Media LLC
Date: 12-07-2008
Publisher: Institute of Mathematical Statistics
Date: 07-2003
Publisher: Cambridge University Press (CUP)
Date: 24-07-2016
DOI: 10.1017/S0266466615000274
Abstract: Limit theory involving stochastic integrals is now widespread in time series econometrics and relies on a few key results on functional weak convergence. In establishing such convergence, the literature commonly uses martingale and semimartingale structures. While these structures have wide relevance, many applications involve a cointegration framework where endogeneity and nonlinearity play major roles and complicate the limit theory. This paper explores weak convergence limit theory to stochastic integral functionals in such settings. We use a novel decomposition of s le covariances of functions of I (1) and I (0) time series that simplifies the asymptotics and our limit results for such covariances hold for linear process, long memory, and mixing variates in the innovations. These results extend earlier findings in the literature, are relevant in many applications, and involve simple conditions that facilitate practical implementation. A nonlinear extension of FM regression is used to illustrate practical application of the methods.
Publisher: No publisher found
Date: 2000
Publisher: Springer Science and Business Media LLC
Date: 10-2005
Publisher: Springer Science and Business Media LLC
Date: 11-12-2009
Publisher: Cambridge University Press (CUP)
Date: 27-08-2011
DOI: 10.1017/S0266466610000277
Abstract: A local limit theorem is given for the s le 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 s le 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.
Publisher: Springer New York
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 15-02-2014
Publisher: Cambridge University Press (CUP)
Date: 06-2009
DOI: 10.1017/S0266466608090269
Abstract: Asymptotic theory is developed for local time density estimation for a general class of functionals of integrated and fractionally integrated time series. The main result provides a convenient basis for developing a limit theory for nonparametric cointegrating regression and nonstationary autoregression. The treatment directly involves local time estimation and the density function of the processes under consideration, providing an alternative approach to the Markov chain and Fourier integral methods that have been used in other recent work on these problems.
Publisher: Cambridge University Press (CUP)
Date: 26-10-2018
DOI: 10.1017/S0266466617000408
Abstract: Limit theory with stochastic integrals plays a major role in time series econometrics. In earlier contributions on weak convergence to stochastic integrals, the literature commonly uses martingale and semi-martingale structures. Liang, Phillips, Wang, and Wang (2016) (see also Wang (2015), Chap. 4.5) currently extended weak convergence to stochastic integrals by allowing for a linear process or a α -mixing sequence in innovations. While these martingale, linear process and α -mixing structures have wide relevance, they are not sufficiently general to cover many econometric applications that have endogeneity and nonlinearity. This paper provides new conditions for weak convergence to stochastic integrals. Our frameworks allow for long memory processes, causal processes, and near-epoch dependence in innovations, which have applications in a wide range of econometric areas such as TAR, bilinear, and other nonlinear models.
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 03-2015
Publisher: Institute of Mathematical Statistics
Date: 2007
DOI: 10.1214/EJP.V12-447
Publisher: Cambridge University Press (CUP)
Date: 06-07-2013
DOI: 10.1017/S0266466612000205
Abstract: 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.
Publisher: Bernoulli Society for Mathematical Statistics and Probability
Date: 08-2013
DOI: 10.3150/12-BEJ415
Publisher: Institute of Mathematical Statistics
Date: 04-2003
Publisher: Springer Science and Business Media LLC
Date: 1996
DOI: 10.1007/BF02009567
Publisher: Cambridge University Press (CUP)
Date: 02-2002
DOI: 10.1017/S0266466602181072
Abstract: Let X t be a linear process defined by X t = [sum ] k=0 ∞ ψ k ε t − k , where {ψ k , k ≥ 0} is a sequence of real numbers and {ε k , k = 0,±1,±2,...} is a sequence of random variables. Two basic results, on the invariance principle of the partial sum process of the X t converging to a standard Wiener process on [0,1], are presented in this paper. In the first result, we assume that the innovations ε k are independent and identically distributed random variables but do not restrict [sum ] k=0 ∞ |ψ k | ∞. We note that, for the partial sum process of the X t converging to a standard Wiener process, the condition [sum ] k=0 ∞ |ψ k | ∞ or stronger conditions are commonly used in previous research. The second result is for the situation where the innovations ε k form a martingale difference sequence. For this result, the commonly used assumption of equal variance of the innovations ε k is weakened. We apply these general results to unit root testing. It turns out that the limit distributions of the Dickey–Fuller test statistic and Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) test statistic still hold for the more general models under very weak conditions.
Publisher: Institute of Mathematical Statistics
Date: 04-2012
DOI: 10.1214/12-AOS975
Publisher: Institute of Mathematical Statistics
Date: 04-2007
Publisher: Bernoulli Society for Mathematical Statistics and Probability
Date: 02-2014
DOI: 10.3150/12-BEJ482
Publisher: Elsevier BV
Date: 11-2004
Publisher: EDP Sciences
Date: 2012
DOI: 10.1051/PS/2010027
Publisher: Institute of Mathematical Statistics
Date: 2013
DOI: 10.1214/13-PS216
Publisher: Cambridge University Press (CUP)
Date: 27-08-2010
DOI: 10.1017/S0266466610000241
Abstract: This paper proposes a model specification testing procedure for parametric specification of the conditional mean function in a nonlinear time series model with long-range dependent. An asymptotically normal test is established even when long-range dependent is involved. To implement the proposed test in practice using a simulated ex le, a bootstrap simulation procedure is established to find a simulated critical value to compute both the size and power values of the proposed test.
Publisher: EDP Sciences
Date: 2011
DOI: 10.1051/PS/2009014
Publisher: Cambridge University Press (CUP)
Date: 25-04-2014
DOI: 10.1017/S026646661400005X
Abstract: Sharp upper and lower uniform bounds are established for a general class of functionals of integrated and fractionally integrated time series. The main result is used to develop optimal uniform convergence for the Nadaraya-Watson estimator and the local linear nonparametric estimator in a nonlinear cointegrating regression model. Unlike the point-wise situation, it is shown that the performance of the local linear nonparametric estimator is superior to that of the Nadaraya-Watson estimator in uniform asymptotics.
Publisher: Springer Science and Business Media LLC
Date: 25-02-2011
Publisher: Institute of Mathematical Statistics
Date: 2009
DOI: 10.1214/EJP.V14-663
Publisher: The Econometric Society
Date: 2009
DOI: 10.3982/ECTA7732
Publisher: Institute of Mathematical Statistics
Date: 04-2004
Publisher: Institute of Mathematical Statistics
Date: 10-1999
Publisher: Springer Science and Business Media LLC
Date: 2003
Publisher: Bernoulli Society for Mathematical Statistics and Probability
Date: 05-2010
DOI: 10.3150/09-BEJ220
Publisher: Institute of Mathematical Statistics
Date: 08-2003
Publisher: Springer Science and Business Media LLC
Date: 07-2004
Publisher: Bernoulli Society for Mathematical Statistics and Probability
Date: 11-2013
DOI: 10.3150/12-BEJ427
Publisher: Cambridge University Press (CUP)
Date: 08-01-2003
Start Date: 2017
End Date: 2019
Funder: Australian Research Council
View Funded ActivityStart Date: 2013
End Date: 2015
Funder: Australian Research Council
View Funded ActivityStart Date: 2004
End Date: 2006
Funder: Australian Research Council
View Funded ActivityStart Date: 2007
End Date: 2011
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2004
End Date: 12-2007
Amount: $225,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2013
End Date: 12-2016
Amount: $180,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 12-2022
Amount: $288,471.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2007
End Date: 12-2012
Amount: $664,070.00
Funder: Australian Research Council
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