Research Interest

• Markov chain Monte Carlo techniques

• Semiparametric and nonparametric estimation

• Panel data models

• Financial econometrics

• Hypothesis testing

• Actuarial studies

Papers submitted

• Panel stochastic frontier models with latent group structures (with Kazuki Tomioka and Thomas T. Yang)

• Cross-market posterior prediction using nonparametric regression under conditional heteroscedasticity, under revision.

• Bayesian estimation for a semiparametric stochastic frontier model with persistent and transient inefficiencies (with P. Nie, V. Zelenyuk and C.F. Parmeter) (Presentation at ESAM 2024), under revision.

• The impact of R&D on energy consumption: Evidence from nonlinear semi-parametric estimations for selected OECD countries (with H. C. Le, R. Smyth and M. Bhattacharya), Revision submitted.

• Revisiting the energy consumption and economic growth nexus: Evidence from a panel of Asia-Pacific countries (with D. Phan and M. Bhattacharya), Revision submitted.

• Time-varying impacts of energy access on income inequality: A semi-parametric analysis (with K. Ivanovski and S. Awaworyi Churchill)

• Empowering energy security: The impact of geopolitical risks on green total factor energy efficiency (with Z. Qiu and Y. Zhang)

Selected publications

• Tsionas, M., V. Zelenyuk, and X. Zhang (2025), Goodness-of-fit in production models: A Bayesian perspective, European Journal of Operational Research, 324(2), 644-653.

• Sun, X., H. Anderson, W. Wei and X. Zhang (2025), Energy transition and climate policy selection with stochastic demand: Evidence from Australian electricity generation expansion planning, Energy Economics, 146, 108397.

• Cheng, M.-Y., S. Wang, L. Xia, and X. Zhang (2024), Testing specification of distribution in stochastic frontier analysis, Journal of Econometrics, 239(2), 105280.

• Le, H.C., M. Bhattacharya, R. Smyth, and X. Zhang (2024), Does economic growth cause energy intensity of well-being in the very long run? Semi-parametric evidence for selected OECD countries, Energy Economics, 139, 107889.

• Dogan, E. and X. Zhang (2023), A nonparametric panel data model for examining the contribution of tourism to economic growth, Economic Modelling, 128, 106487

• Yuan, Z., X. Zhang, J. Wang, and X. Cheng (2022), A flexible method for estimating luminosity functions via kernel density estimation - II. Generalization and Python code, Astrophysical Journal Supplement Series, 260(10).

• Shang, H.L. and X. Zhang (2022), Bayesian bandwidth estimation for local linear fitting in nonparametric regression models, Studies in Nonlinear Dynamics & Econometrics, 26(1), 55-71.

• Meng, Y., J. Gao, X. Zhang, and X. Zhao (2021), A panel data model of length of stay in hospitals for hip replacements, Econometric Reviews, 40(7), 688-707.

• Hu, S., D.S. Poskitt, and X. Zhang (2021), Bayesian estimation for a semiparametric nonlinear volatility model, Economic Modelling, 98, 361-370.

• King, M.L., X. Zhang, M. Akram (2020), Hypothesis testing based on a vector of statistics, Journal of Econometrics, 219(2), 425-455 [Free Access][Online supplementary document].

• Liddle, B., R. Smyth, and X. Zhang (2020), Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel, Energy Economics, 86, 104681.

• Cheng, T., J. Gao, and X. Zhang (2019), Bayesian bandwidth selection in nonparametric time-varying coefficient models, Journal of Business & Economic Statistics, 37(1), 1-12.

• Hailemariam, A., R. Smyth, and X. Zhang (2019), Oil prices and economic policy uncertainty: Evidence from a nonparametric panel data model, Energy Economics, 83, 40-51.

• Awaworyi Churchill, S., J. Inekwe, R. Smyth, and X. Zhang (2019), R&D intensity and carbon emissions in the G7: 1870-2014, Energy Economics, 80, 30-37.

• Feng, G., C. Wang, and X. Zhang (2019), Estimation of inefficiency in stochastic frontier models: A Bayesian kernel approach, Journal of Productivity Analysis, 51(1), 1-19.

• Cheng, T., J. Gao, and X. Zhang (2019), Nonparametric localized bandwidth selection for kernel density estimation, Econometric Reviews, 38(7), 733-762.

• Chen, H., R. Smyth, and X. Zhang (2017), A Bayesian sampling approach to measuring the price responsiveness of gasoline demand using a constrained partially linear model, Energy Economics, 67, 346-354.

• Silvapulle, P., R. Smyth, X. Zhang, and J.-P. Fenech (2017), Nonparametric panel data model for crude oil and stock market prices in net oil importing countries, Energy Economics, 67, 255-267.

• Zhang, X., M.L. King, and H.L. Shang (2016), Bayesian bandwidth selection for a nonparametric regression model with mixed types of regressors, Econometrics, 4(2), 24.

• Li, H., C. O'Hare, and X. Zhang (2015), A semiparametric approach to mortality modeling, Insurance: Mathematics and Economics, 61, 264-270.

• Cai, Z., J. Jiang, J. Zhang, and X. Zhang (2015), A new semiparametric test for superior predictive ability, Empirical Economics, 48(1), 389-405.

• Zhang, R., B. Inder, and X. Zhang (2015), Bayesian estimation of a discrete response model with double rules of sample selection, Computational Statistics & Data Analysis, 86(1), 81-96.

• Li, S., M. Silvapulle, P. Silvapulle, and X. Zhang (2015), Bayesian approaches to nonparametric estimation of densities on the unit interval, Econometric Reviews, 34(3), 394-412.

• Zhang, X., M.L. King, and H.L. Shang (2014), A sampling algorithm for bandwidth estimation in a nonparametric regression model with a flexible error density, Computational Statistics & Data Analysis, 78, 218-234.

• Hu, S., D.S. Poskitt, and X. Zhang (2012), Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions, Computational Statistics & Data Analysis, 56, 732-740.

• Liu, Q., D. Pitt, X. Zhang, and X. Wu (2011), A Bayesian approach to parameter estimation for kernel density estimation via transformations, Annals of Actuarial Science, 5(2), 181-193.

• Dark, J., X. Zhang, and N. Qu (2010), Influence diagnostics for multivariate GARCH processes, Journal of Time Series Analysis, 31, 278-291.

• Zhang, X., R.D. Brooks and M.L. King (2009), A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation, Journal of Econometrics, 153, 21-32.

• Zhang, X. and M.L. King (2008), Box-Cox stochastic volatility models with heavy-tails and correlated errors, Journal of Empirical Finance, 15, 549-566.

• Brooks, R.D., X. Zhang, and E. Bissoondoyal-Bheenick (2007), Country risk and the estimation of asset return distributions, Quantitative Finance, 7, 261-265.

• Yu, J., Z. Yang and X. Zhang (2006), A class of nonlinear stochastic volatility models and its implications for pricing currency options, Computational Statistics & Data Analysis, 51, 2218-2231.

• Zhang, X., M.L. King and R.J. Hyndman (2006), A Bayesian approach to bandwidth selection for multivariate kernel density estimation, Computational Statistics & Data Analysis, 50, 3009-3031.

• Pitrun, I., M.L. King, and X. Zhang (2006), Smoothing spline based tests for non-linearity in a partially linear model, Journal of Statistical Planning and Inference, 136, 2446-2469.

• Zhang, X. and M.L. King (2005), Influence diagnostics in generalized autoregressive conditional heteroskedasticity processes, Journal of Business & Economic Statistics, 23, 118-129 (Errata).

• Zhang, X. (2004), Assessment of local influence in GARCH processes, Journal of Time Series Analysis, 25, 301-313.

• Tse, Y.K., K.W. Ng, and X. Zhang (2004), A small-sample overlapping variance-ratio test, Journal of Time Series Analysis, 25, 127-135.

• Tse, Y.K., X. Zhang, and J. Yu (2004), Estimation of hyperbolic diffusion using the Markov chain Monte Carlo method, Quantitative Finance, 4, 158-169.

• Tse, Y.K. and X. Zhang (2004), A Monte Carlo investigation of some tests for stochastic dominance, Journal of Statistical Computation and Simulation, 74, 361-378.

• Lien, D., Y.K. Tse, and X. Zhang (2003), Structural change and lead-lag relationship between the Nikkei spot index and futures price: A genetic programming approach, Quantitative Finance, 3, 136-144.

• Tse, Y.K. and X. Zhang (2002), The variance ratio test with Stable Paretian errors, Journal of Time Series Analysis, 23, 117-126 [Extended Tables and Graphs].

• Tse, Y.K. and X. Zhang (2001), Local influence on bandwidth estimation for kernel smoothing, Journal of Statistical Computation and Simulation, 70, 349-370.

Work in progress

• Gaussian Kernel GARCH Models (with M.L. King)

• Conditional Heteroscedasticity Models with Time-Varying Parameters: Estimation and Asymptotics (with A. Pourkhanali and J. Keith)

Awards

2009 Dean's Award for Excellence in Research

2007 Dean's Commendation for Excellence in Research Publication

Competitive Research Grants

2017-2018: BEYOND THE CITY: Increasing the dispersal of Chinese visitors in regional Australia. Funded by the Department of Foreign Affairs and Trade (DFAT), Australia

2013-2015: Gao, J., X. Zhang and D.B. Tjøstheim, Trending time series models with non- and semi-parametric methods, Australian Research Council Discovery Project.

2010-2012: Zhang, X. and M.L. King, Nonparametric estimation of regression models with unknown error distributions, Australian Research Council Discovery Project.

2006-2008: King, M.L. and X. Zhang, New procedures for multiple testing of econometric models, Australian Research Council Discovery Project.

Referees

Journals

Research Grant Organisations