Publications

• 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.

• 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.

• 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.

• 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., 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.