Essays in Honor of Jerry Hausman
Essays in Honor of Jerry Hausman
ISBN: 978-1-78190-307-0, eISBN: 978-1-78190-308-7
ISSN: 0731-9053
Publication date: 19 December 2012
Citation
(2012), "Essays in Honor of Jerry Hausman", Baltagi, B.H., Carter Hill, R., Newey, W.K. and White, H.L. (Ed.) Essays in Honor of Jerry Hausman (Advances in Econometrics, Vol. 29), Emerald Group Publishing Limited, Leeds, p. i. https://doi.org/10.1108/S0731-9053(2012)0000029024
Publisher
:Emerald Group Publishing Limited
Copyright © 2012, Emerald Group Publishing Limited
- Essays in Honor of Jerry Hausman
- Advances in Econometrics
- Advances in Econometrics
- Copyright Page
- List of Contributors
- The Genesis of the Hausman Specification Test
- Introduction
- The Diffusion of Hausman's Econometric Ideas
- Combining Two Consistent Estimators
- A Minimum Mean Squared Error Semiparametric Combining Estimator
- An Expository Note on the Existence of Moments of Fuller and HFUL Estimators
- Overcoming the Many Weak Instrument Problem Using Normalized Principal Components
- Errors-in-Variables and the Wavelet Multiresolution Approximation Approach: A Monte Carlo Study
- A Robust Hausman–Taylor Estimator
- Small Sample Properties and Pretest Estimation of a Spatial Hausman–Taylor Model
- Quantile Regression Estimation of Panel Duration Models with Censored Data
- Labor Allocation in a Household and its Impact on Production Efficiency: A Comparison of Panel Modeling Approaches
- Using Panel Data to Examine Racial and Gender Differences in Debt Burdens
- Sovereign Bond Spread Drivers in the EU Market in the Aftermath of the Global Financial Crisis
- Conditional Independence Specification Testing for Dependent Processes with Local Polynomial Quantile Regression
- Extending the Hausman Test to Check for the Presence of Outliers
- A Simple Test for Identification in GMM under Conditional Moment Restrictions
- Fixed vs Random: The Hausman Test Four Decades Later
- The Hausman Test, and Some Alternatives, with Heteroskedastic Data
- A Hausman Test for Spatial Regression Model