Papers Submitted

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

• Technology adoption, domestic innovation, and total factor productivity growth and convergence: Evidence from 117 years of OECD data (with J.W.Y. Leung and Param Silvapulle), submitted

Selected Publications

• Tomioka, K., T.T. Yang and X. Zhang (2026), Panel Stochastic Frontier Models with Latent Group Structures, Journal of Business & Economic Statistics (Ranked A* by ABDC), forthcoming.

• Nie, P., C.F. Parmeter, V. Zelenyuk, and X. Zhang (2026), Bayesian estimation for a semiparametric stochastic frontier model with persistent and transient inefficiencies, Journal of Econometrics (Ranked A* by ABDC), 256, 106081.

• Leung, J.W.Y., P. Nie, R. Smyth, L. Zhang, Q. Zhang and X. Zhang (2026), Persistent and transient inefficiency in China’s provincial energy demand: A four-component stochastic frontier model, Energy Economics (Ranked A* by ABDC), forthcoming.

• Ivanovski, K., R. Smyth, and X. Zhang (2026), Time-varying human capital and energy consumption for a panel of OECD countries in the long-run, Energy Economics, 155, 109205.

• Chen, G., P. Nie and X. Zhang (2026), Efficiency in producing life satisfaction: A stochastic frontier analysis, Economics Letters (Ranked A by ABDC), 267, 113097.

• Qiu, Z., X. Zhang and Y. Zhang (2025), Empowering energy security: The impact of geopolitical risks on green total factor energy efficiency, Energy Economics, 151, 108924.

• Le, H.C., M. Bhattacharya, R. Smyth, and X. Zhang (2025), The impact of R&D on energy consumption: Evidence from nonlinear semi-parametric estimations for selected OECD countries, Energy Economics, 149, 108708.

• Tsionas, M., V. Zelenyuk, and X. Zhang (2025), Goodness-of-fit in production models: A Bayesian perspective, European Journal of Operational Research (Ranked A* by ABDC), 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.

• Ivanovski, K., S. Awaworyi Churchill and X. Zhang (2025), Time-varying impacts of energy access on income inequality: A semi-parametric analysis, Environmental and Ecological Statistics (Ranked A by ABDC), 32, 1431-1453.

• 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 (Ranked A by ABDC), 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 (Ranked A by ERA 2010 Initiative), 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 (Ranked A by ABDC), 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 (Ranked A by ABDC), 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 (Ranked A by ABDC), 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 (Ranked A* by ABDC), 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)

• Testing the Effects of Exogenous Determinants on Inefficiency in Panel Stochastic Frontier Models (with Richard Luger)

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