Showing 1 - 10 of 1,263
-sample performance of our estimators. An empirical study of efficiency trends in the largest banks operating in the U.S. from 1990 to …
Persistent link: https://www.econbiz.de/10011995241
-sample performance of our estimators. An empirical study of efficiency trends in the largest banks operating in the U.S. from 1990 to …
Persistent link: https://www.econbiz.de/10011711007
We consider a Bayesian analysis of the stochastic frontier model with composed error.Under a commonly used class of (partly) noninformative prior distributions, the existence of the posterior distribution and of posterior moments is examined.Viewing this model as a Normal linear regression model...
Persistent link: https://www.econbiz.de/10011090882
Using a stochastic frontier model and a comprehensive dataset, we study factors that affect corporate efficiency in … efficiency, and (iii) high competition is less conductive to efficiency than moderate or low competition. In terms of ownership …, we find that (iv) efficiency increases when a majority owner must deal with minority shareholders and that (v) domestic …
Persistent link: https://www.econbiz.de/10010510115
We use a dynamic panel Tobit model with heteroskedasticity to generate forecasts for a large cross-section of short time series of censored observations. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of heterogeneous coefficients and then implicitly...
Persistent link: https://www.econbiz.de/10014536986
This paper extends the work of Baltagi et al. (2018) to the popular dynamic panel data model. We investigate the robustness of Bayesian panel data models to possible misspecication of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in...
Persistent link: https://www.econbiz.de/10012269892
This paper extends the Baltagi et al. (2018, 2021) static and dynamic ?-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the...
Persistent link: https://www.econbiz.de/10014296559
The paper develops a general Bayesian framework for robust linear static panel data models using ε-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects. The ML-II posterior...
Persistent link: https://www.econbiz.de/10010468186
This paper extends the work of Baltagi et al. (2018) to the popular dynamic panel data model. We investigate the robustness of Bayesian panel data models to possible misspecication of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in...
Persistent link: https://www.econbiz.de/10012210757
The paper develops a general Bayesian framework for robust linear static panel data models using ε-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects. The ML-II posterior...
Persistent link: https://www.econbiz.de/10010440546