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Gradient-based smoothing parameter selection for nonparametric regression estimation

Henderson, Daniel J.; Li, Qi; Parmeter, Christopher F.; … - In: Journal of econometrics 184 (2015) 2, pp. 233-241

Persistent link: https://www.econbiz.de/10011339349

Due diligence in meta-analyses to support benefit transfers

Boyle, Kevin J.; Parmeter, Christopher F.; Boehlert, … - In: Environmental & resource economics : the official … 55 (2013) 3, pp. 357-386

Persistent link: https://www.econbiz.de/10009753954

Who benefits from financial development? : new methods, new evidence

Henderson, Daniel J.; Papageorgiou, Chris; Parmeter, … - In: European economic review : EER 63 (2013), pp. 47-67

Persistent link: https://www.econbiz.de/10010233902

When is it justifiable to ignore explanatory variable endogeneity in a regression model?

Ashley, Richard A.; Parmeter, Christopher F. - In: Economics letters 137 (2015), pp. 70-74

Persistent link: https://www.econbiz.de/10011436234

Smooth coefficient estimation of a seemingly unrelated regression

Henderson, Daniel J.; Kumbhakar, Subal; Li, Qi; … - In: Journal of econometrics 189 (2015) 1, pp. 148-162

Persistent link: https://www.econbiz.de/10011502513

A simple estimator for partial linear regression with endogenous nonparametric variables

Delgado, Michael S.; Parmeter, Christopher F. - In: Economics letters 124 (2014) 1, pp. 100-103

Persistent link: https://www.econbiz.de/10010490581

Estimation and inference under economic restrictions

Parmeter, Christopher F.; Sun, Kai; Henderson, Daniel J.; … - In: Journal of productivity analysis 41 (2014) 1, pp. 111-129

Persistent link: https://www.econbiz.de/10010429332

A simple method to visualize results in nonlinear regression models

Henderson, Daniel J.; Kumbhakar, Subal; Parmeter, … - In: Economics letters 117 (2012) 3, pp. 578-581

Persistent link: https://www.econbiz.de/10009679148

A simple method to visualize results in nonlinear regression models

Henderson, Daniel J.; Kumbhakar, Subal; Parmeter, … - 2012

A simple graphical approach to presenting results from nonlinear regression models is described. In the face of multiple covariates, 'partial mean' plots may be unattractive. The approach here is portable to a variety of settings and can be tailored to the specific application at hand. A simple...

Persistent link: https://www.econbiz.de/10009580558

Nonparametric Kernel regression with multiple predictors and multiple shape constraints

Du, Pang; Parmeter, Christopher F.; Racine, Jeffrey - 2012

Persistent link: https://www.econbiz.de/10009612415