Statistical Modelling of Interdependence in Economics
Lead Research Organisation:
Brunel University London
Department Name: Finance
Abstract
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Organisations
Publications
Baltagi B
(2010)
Health care expenditure and income in the OECD reconsidered: Evidence from panel data
in Economic Modelling
Baltagi B
(2011)
Medical technology and the production of health care
in Empirical Economics
Berta P
(2016)
The Association Between Asymmetric Information, Hospital Competition and Quality of Healthcare: Evidence from Italy
in Journal of the Royal Statistical Society Series A: Statistics in Society
Chudik A
(2011)
Weak and strong cross-section dependence and estimation of large panels
in The Econometrics Journal
Francesco Moscone (Author)
(2009)
Testing for error cross section independence in panels
Lagravinese R
(2014)
The impact of air pollution on hospital admissions: Evidence from Italy
in Regional Science and Urban Economics
McMillen D
(2014)
Special issue on health econometrics: Editors' introduction
in Regional Science and Urban Economics
Moscone F
(2012)
Social Interaction in Patients' Hospital Choice: Evidence from Italy
in Journal of the Royal Statistical Society Series A: Statistics in Society
Moscone F
(2011)
Geographical variations in expenditure of learning disability services in England
in Applied Economics
Moscone F
(2010)
Health expenditure and income in the United States.
in Health economics
| Title | Matlab codes for estimation of panel data with cross section dependence |
| Description | Codes for the mean group and pooled estimators, the common correlated effects mean group and the common correlated effects pooled estimators. See Pesaran H., and Tosetti E. (2011) "Large Panels with Common Factors and Spatial Correlations", Journal of Econometrics, 161, 182-202 |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2012 |
| Provided To Others? | Yes |
| Description | GMM estimation of dynamic panels with spatially dependent errors : an application to credit risk and financial stability |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | We focus on generalized method of moments (GMM) for estimating first-order dynamic panel data models with group-specific effects and errors following a spatial autoregressive process. We suggest to augment moments proposed by the panel literature to estimate pure dynamic panels, with a set of quadratic conditions in the disturbances. We prove consistency and asymptotic normality of the GMM estimator of the slope parameters and of the spatial autoregressive coefficient, for N going to infinity and T fixed. Hence, we propose a two-step estimator for the regression parameters, corrected for spatial dependence. We investigate the small sample properties of this method by the means of a set of Monte Carlo experiments, under various assumptions on the time series persistence, and on the degree of spatial dependence in the error term. Finally, the usefulness of the suggested procedure is tested by estimating a model on the macroeconomic determinants of credit risk. |
| Year(s) Of Engagement Activity | 2008 |
| Description | GMM estimation of spatial panels with fixed effects and unknown heteroskedasticity |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | In this paper we consider the estimation of a panel data regression model with spatial autoregressive disturbances, fixed effects and unknown heteroskedasticity. We adopt the Generalized Method of Moments (GMM) and consider as moments a set linear quadratic conditions in the disturbances. We assume that the inner matrices in the quadratic forms have zero diagonal elements to robustify moments against unknown heteroskedasticity. We derive the asymptotic distribution of the GMM estimator based on such conditions. Hence, we carry out some Monte Carlo experiments to investigate the small sample properties of GMM estimators based on various sets of moment conditions. |
| Year(s) Of Engagement Activity | 2008 |