Generalised Anderson-Rubin statistic based inference in the presence of a singular moment variance matrix

Nicky L. Grant and Richard J. Smith
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Nicky L. Grant: Institute for Fiscal Studies
Richard J. Smith: Institute for Fiscal Studies

No CWP05/19, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: The particular concern of this paper is the construction of a confidence region with pointwise asymptotically correct size for the true value of a parameter of interest based on the generalized Anderson-Rubin (GAR) statistic when the moment variance matrix is singular. The large sample behaviour of the GAR statistic is analysed using a Laurent series expansion around the points of moment variance singularity. Under a condition termed first order moment singularity the GAR statistic is shown to possess a limiting chi-square distribution on parameter sequences converging to the true parameter value. Violation, however, of this condition renders the GAR statistic unbounded asymptotically. The paper details an appropriate discretisation of the parameter space to implement a feasible GAR-based confidence region that contains the true parameter value with pointwise asymptotically correct size. Simulation evidence is provided that demonstrates the efficacy of the GAR-based approach to moment-based inference described in this paper.

Keywords: Laurent series expansion; moment indicator; parameter sequences; singular moment matrix (search for similar items in EconPapers)
JEL-codes: C13 C31 C51 C52 (search for similar items in EconPapers)
Date: 2019-01-30
New Economics Papers: this item is included in nep-ecm and nep-ore
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