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Quantile smoothing splines

Koenker, Roger; Ng, Pin T.; Portnoy, Steven - 1993

Persistent link: https://www.econbiz.de/10000866011

Using quantile regression to understand visitor spending

Lew, Alan A.; Ng, Pin T. - In: Journal of travel research : a quarterly publication of … 51 (2012) 3, pp. 278-288

Persistent link: https://www.econbiz.de/10009547198

No matter how it is measured, income declines with global warming

Ng, Pin T.; Zhao, Xiaobing - In: Ecological economics : the transdisciplinary journal of … 70 (2011) 5, pp. 963-970

Persistent link: https://www.econbiz.de/10009247382

Refining our understanding of beta through quantile regressions

Atkins, Allen B.; Ng, Pin T. - In: Journal of risk and financial management : JRFM 7 (2014) 2, pp. 67-79

The Capital Asset Pricing Model (CAPM) has been a key theory in financial economics since the 1960s. One of its main contributions is to attempt to identify how the risk of a particular stock is related to the risk of the overall stock market using the risk measure Beta. If the relationship...

Persistent link: https://www.econbiz.de/10011553298

Measuring currency exposure with quantile regression

Du, Ding; Ng, Pin T.; Zhao, Xiaobing - In: Review of quantitative finance and accounting 41 (2013) 3, pp. 549-566

Persistent link: https://www.econbiz.de/10010188239

Mincer-Zarnowitz quantile and expectile regressions for forecast evaluations under aysmmetric loss functions

Güler, Kemal; Ng, Pin T.; Xiao, Zhijie - In: Journal of forecasting 36 (2017) 6, pp. 651-679

Persistent link: https://www.econbiz.de/10011861402

Stochastic dominance via quantile regression with applications to investigate arbitrage opportunity and market efficiency

Ng, Pin T.; Wong, Wing Keung; Xiao, Zhijie - In: European journal of operational research : EJOR 261 (2017) 2, pp. 666-678

Persistent link: https://www.econbiz.de/10011738062

Urban house price surfaces near a World Heritage Site : modeling conditional price and spatial heterogeneity

Fritsch, Markus; Haupt, Harry; Ng, Pin T. - In: Regional science & urban economics 60 (2016), pp. 260-275

Persistent link: https://www.econbiz.de/10011639155