Nonparametric estimation homothetic and homothetically separable functions

Arbeitspapier

For vectors x and w, let r(x,w) be a function that can be nonparametrically estimated consistently and asymptotically normally. We provide consistent, asymptotically normal estimators for the functions g and h, where r(x,w) = h[g(x),w], g is linearly homogeneous and h is monotonic in g. This framework encompasses homothetic and homothetically separable functions. Such models reduce the curse of dimensionality, provide a natural generalization of linear index models, and are widely used in utility, production, and cost function applications. Extensions to related functional forms include a generalized partly linear model with unknown link function. We provide simulation evidence on the small sample performance of our estimator, and we apply our method to a Chinese production dataset.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP14/03

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
• Subject: Cost Function , Economies of Scale , Homogeneous Function , Homothetic Function , Index Models , Nonparametric , Production Function , Separability ; Schätzung ; Nichtparametrisches Verfahren ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Lewbel, Arthur ; Linton, Oliver Bruce
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2003

Further information

• DOI: doi:10.1920/wp.cem.2003.1403
• Handle: http://hdl.handle.net/10419/79332
• Last update: 10.03.2025, 11:42 AM CET