Asymptotic inference from multi-stage samples

By Debopam Bhattacharya. 2005

59 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/008b6a05fbe162da491739f620060d1f0bcff2cf

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Citations in the corpus

Statistical Inference with Generalized Gini Indices of Inequality, Poverty, and Welfare (2009, methodology): “It is also possible to allow for more complex sampling schemes in the estimation of the Gini indices and asymptotic variances using the framework presented in Bhattacharya (2005).”
Breaking the Caste Barrier: Intergenerational Mobility in India (2013, methodology): “29 Next we compute a more direct measure of upward mobility, as proposed by Bhattacharya and Mazumder (2011). It is computed as the probability that a son’s income rank in his generational income distribution exceeds the income rank of his father in the income distribution of father’s generation.”
A Practitioner’s Guide to Cluster-Robust Inference (2015, methodology): “The gains can become much smaller once regressors are included because these can partially control for stratifi cation; see, for example, the application in Bhattacharya (2005).”
Insurer pricing and consumer welfare: evidence from Medigap (2014, methodology): “Furthermore, the market shares are adjusted to account for consumers who appear in multiple cross-sections, and the standard errors are corrected using the method described by Bhattacharya [2005]. The data contain information about new policies sold; there are a few advantages to this measure.”
Quantile Regression with Clustered Data (2015, methodology): “The consistency of β̂ can be proved under the following assumption: of the covariance matrix of the quantile regression estimator requires the definition of a method to estimate the conditional density at zero, a problem that is not considered in Bhattacharya (2005).”
Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India (2004, background): “Under assumptions A0-A8, √ n(β̂ − β0) d → N(0,Γ−1W (Γ−1)′) Proof: It is sufficient to note that assumptions A1-A8 correspond to assumptions A0-A7 and A8b in Bhattacharya (2005). Then the conclusion follows from his Proposition 2, for the case where the number of moments is equal to the number of est”
Consistent Nonparametric Tests for Lorenz Dominance (2014, methodology): “Proof of Proposition 1: The LC is a Hadamard differentiable functional of the empirical distribution function following the results by Bhattacharya (2005).”