Semiparametric Bayesian Inference for Stochastic Frontier Models

By Jim E. Griffin, Mark F. J. Steel. 2004

118 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f1130512e275a806b07ee32d78953e6175cd22f5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

Satisfaction, Complaint, and the Stock Value Gap (2008, methodology): “This approach (SFM4) incorporates the semiparametric Bayesian inference with an efficient Markov chain Monte Carlo sampler (Griffin and Steel 2004).”
Stick-breaking autoregressive processes (2011, methodology): “…in economics include autoregressive panel data models (Hirano, 2002), longitudinal data treatment models (Chib and Hamilton, 2002) and stochastic frontiers (Griffin and Steel, 2004). and James, 2001, 2003) (which will be discussed in section 2), and Normalized Random Measures (James et al., 2009).”
Bayesian density regression (2007, methodology): “Mezzetti, and Muliere (2003), and Grin and Steel (2004) .”
Smoothly mixing regressions (2007, background): “Griffin and Steel (2004) take a similar approach in a stochastic production frontier model, concentrating flexibility on the firm component.”
Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility (2011, unknown): “The mass parameter of the Dirichlet process, M , has the prior suggested by Griffin and Steel (2004), p (M) = θλ Γ (2λ) (Γ (λ)) 2 Mλ−1 (M + θ) 2λ where θ denotes a prior sample size and λ is a variance parameter.”
Bayesian semiparametric stochastic volatility modeling (2010, methodology): “, n, Equation (6) equals the the autoregressive, stochastic volatility model of Jacquier et al. (1994). The SV-t model of Harvey et al.”
Order-Based Dependent Dirichlet Processes (2006, methodology): “which was introduced by Griffin and Steel (2004) and where the hyperparameter n0 > 0 is the prior median ofM and the prior variance of M (which exists ifη > 2) is a decreasing function of η.”
Comparing distributions by using dependent normalized random‐measure mixtures (2013, methodology): “…of the efficiency distribution with just a single group (averaged over all hospital types) has three internal modes at roughly 0.65, 0.7 and 0.8 and a further mode at 1, which is quite in line with the results for the efficiency that were obtained in Griffin and Steel (2004) based on all the data.”
A General Framework for the Parametrization of Hierarchical Models (2007, background): “, [17]), which in their simplest form are written as”