1,721,039 research outputs found
Moscone, F., Tosetti, E. (2010), Testing for Spatial Correlation in Panels with Unobserved Common Effects: an Application to US Health Expenditure
No full textRobust estimation under error cross section dependence
Full text linkThis article has been made available through the Brunel Open Access Publishing Fund.We propose a robust, partial sample estimator for the covariance matrix of the fixed effects and mean
group estimators of the slope coefficients in a short T panel data model with group-specific effects and
errors that are weakly cross sectionally dependent and serially correlated
Geographical variations in expenditure of Learning Disability Services in England
No full textThis article investigates the determinants of local authority Learning Disability (LD) expenditure in England. It adopts a reduced form of demand and supply model, extended to account for possible interdependence between municipalities. Risk factors such as 'people aged under 14', 'mortality rate' and 'lone parents' seem to play an important role in explaining geographical variation of spending. Further, labour municipalities on average allocate lower resources on LD than do other political parties. Finally, results corroborate recent findings in economics that authorities interact with each other when allocating public resources. © 2011 Taylor & Francis
HAC estimation in spatial panels
Full text link© 2012 Elsevier B.V. All rights reservedWe propose a HAC estimator for the covariance matrix of the fixed effects estimator in a panel data model with unobserved fixed effects and errors that are both serially and spatially correlated.conomic and Social Research Council (grant RES-061-25-0317)
GMM estimation of Spatial Panels with Fixed Effects and Unknown Heteroskedasticity
No full textIn this paper we consider the estimation of a panel data regression model with spatial autoregressive disturbances, fixed effects and unknown heteroskedasticity. Following the work by Kelejian and Prucha (1999), Lee and Liu (2006a) and others, we adopt the Generalized Method of Moments (GMM) and consider moments as a set of linear quadratic conditions in the disturbances. As in Lee and Liu (2006a), 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. © 2011
A review and comparison of tests of cross section independence in panels
No full textIn this paper we review and compare diagnostic tests of cross-section independence in the disturbances of panel regression models. We examine tests based on the sample pairwise correlation coefficient or on its transformations, and tests based on the theory of spacings. The ultimate goal is to shed some light on the appropriate use of existing diagnostic tests for cross-equation error correlation. Our discussion is supported by means of a set of Monte Carlo experiments and a small empirical study on health. Results show that tests based on the average of pairwise correlation coefficients work well when the alternative hypothesis is a factor model with non-zero mean loadings. Tests based on spacings are powerful in identifying various forms of strong cross-section dependence, but have low power when they are used to capture spatial correlation. © Journal compilation © 2009 Blackwell Publishing Ltd
Health expenditure and income in the United States
No full textThis paper investigates the long-run economic relationship between health care expenditure and income in the US at a State level. Using a panel of 49 US States over the period 1980-2004, we study the non-stationarity and co-integration between health spending and income, ultimately measuring income elasticity of health care. The tests we adopt allow us to explicitly control for cross-section dependence and unobserved heterogeneity. Specifically, in our regression equations we assume that the error has a multifactor structure, which may capture global shocks and local spill overs in health expenditure. Our results suggest that health care is a necessity rather than a luxury, with an elasticity much smaller than that estimated in other US studies. Further, we detect significant spatial concentration in US health spending. Our broad perspective of cross-section dependence as well as the methods used to capture it give new insights on the debate over the relationship between health spending and income. Copyright © 2009 John Wiley & Sons, Ltd
Testing for error cross section independence with an application to US health expenditure
No full textThis paper considers the problem of testing for error cross section independence in a panel where statistical units may be subject to unobserved common effects, spatial spill overs, or both. We review a number of diagnostics that are used for testing for error cross section independence in panels, including tests based on spacings and spatial statistics. We then argue that commonly used spatial statistics might give misleading results when cross section correlation arising from common effects is not taken into account. Hence, we study the properties of spatial statistics applied to residuals obtained from an augmented regression, where common factors have been approximated by principal components (Bai, 2009). Small sample properties of our testing strategy are investigated in a Monte Carlo study. Results show that spatial tests applied to de-factored residuals detect well the presence of spatial correlation in the data. The paper concludes with a small empirical exercise on US health expenditure. © 2009 Elsevier B.V
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