R&D intensity and carbon emissions in the G7: 1870–2014 SA Churchill, J Inekwe, R Smyth, X Zhang Energy Economics 80, 30-37, 2019 | 392 | 2019 |
A Bayesian approach to bandwidth selection for multivariate kernel density estimation X Zhang, ML King, RJ Hyndman Computational Statistics & Data Analysis 50 (11), 3009-3031, 2006 | 250 | 2006 |
Oil prices and economic policy uncertainty: Evidence from a nonparametric panel data model A Hailemariam, R Smyth, X Zhang Energy economics 83, 40-51, 2019 | 184 | 2019 |
Nonparametric panel data model for crude oil and stock market prices in net oil importing countries P Silvapulle, R Smyth, X Zhang, JP Fenech Energy economics 67, 255-267, 2017 | 150 | 2017 |
A Monte Carlo investigation of some tests for stochastic dominance YK Tse, X Zhang Journal of statistical computation and simulation 74 (5), 361-378, 2004 | 114 | 2004 |
The sizes and powers of some stochastic dominance tests: A Monte Carlo study for correlated and heteroskedastic distributions HH Lean, WK Wong, X Zhang Mathematics and Computers in Simulation 79 (1), 30-48, 2008 | 90 | 2008 |
A class of nonlinear stochastic volatility models and its implications for pricing currency options J Yu, Z Yang, X Zhang Computational Statistics & Data Analysis 51 (4), 2218-2231, 2006 | 71* | 2006 |
A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation X Zhang, RD Brooks, ML King Journal of Econometrics 153 (1), 21-32, 2009 | 69 | 2009 |
Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel B Liddle, R Smyth, X Zhang Energy economics 86, 104681, 2020 | 58 | 2020 |
Estimation of hyperbolic diffusion using the Markov chainMonte Carlo method YK Tse, X Zhang, J Yu Quantitative Finance 4 (2), 158, 2003 | 55* | 2003 |
Influence diagnostics in generalized autoregressive conditional heteroscedasticity processes X Zhang, ML King Journal of Business & Economic Statistics 23 (1), 118-129, 2005 | 49 | 2005 |
Box-Cox stochastic volatility models with heavy-tails and correlated errors X Zhang, ML King Journal of Empirical Finance 15 (3), 549-566, 2008 | 42 | 2008 |
A semiparametric panel approach to mortality modeling H Li, C O’Hare, X Zhang Insurance: Mathematics and Economics 61, 264-270, 2015 | 32 | 2015 |
Structural change andlead-lag relationship between the Nikkei spot index and futuresprice: a genetic programming approach D Lien, YK Tse, X Zhang Quantitative Finance 3 (2), 136, 2003 | 29 | 2003 |
Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions S Hu, DS Poskitt, X Zhang Computational Statistics & Data Analysis 56 (3), 732-740, 2012 | 28 | 2012 |
Nonparametric localized bandwidth selection for Kernel density estimation T Cheng, J Gao, X Zhang Econometric Reviews 38 (7), 733-762, 2019 | 22* | 2019 |
A sampling algorithm for bandwidth estimation in a nonparametric regression model with a flexible error density X Zhang, ML King, HL Shang Computational Statistics & Data Analysis 78, 218-234, 2014 | 22* | 2014 |
Assessment of local influence in GARCH processes X Zhang Journal of Time Series Analysis 25 (2), 301-313, 2004 | 20 | 2004 |
Gaussian kernel GARCH models X Zhang, ML King Monash Econometrics and Business Statistics Working Papers 19, 13, 2013 | 17* | 2013 |
A small‐sample overlapping variance‐ratio test YK Tse, KW Ng, X Zhang Journal of Time Series Analysis 25 (1), 127-135, 2004 | 16 | 2004 |