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Estimation and Inference of Discontinuity in Density

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[16] Marron, J. S. <strong>and</strong> D. Ruppert (1994) Transformations to reduce boundary bias <strong>in</strong> kernel densityestimation, Journal <strong>of</strong> the Royal Statistical Society, B, 56, 653-671.[17] McCrary, J. (2008) Manipulation <strong>of</strong> the runn<strong>in</strong>g variable <strong>in</strong> the regression discont<strong>in</strong>uity design: adensity test, Journal <strong>of</strong> Econometrics, 142, 698-714.[18] Newey, W. K. <strong>and</strong> R. J. Smith (2004) Higher order properties <strong>of</strong> GMM <strong>and</strong> generalized empiricallikelihood estimators, Econometrica, 72, 219-255.[19] Owen, A. (2001) Empirical Likelihood, Chapman & Hall, New York.[20] Porter, J. (2003) <strong>Estimation</strong> <strong>in</strong> the regression discont<strong>in</strong>uity model. Work<strong>in</strong>g paper, Department <strong>of</strong>Economics, University <strong>of</strong> Wiscons<strong>in</strong>.[21] Saez, E. (2009) Do taxpayers bunch at k<strong>in</strong>k po<strong>in</strong>ts? forthcom<strong>in</strong>g <strong>in</strong> American Economic Journal:Economic Policy.[22] Xu, K.-L. <strong>and</strong> P. C. B. Phillips (2007) Tilted nonparametric estimation <strong>of</strong> volatility functions withempirical applications. Cowles Foundation Discussion Paper 1612R, Yale University.[23] Xu, K.-L. (2010). Re-weighted Functional <strong>Estimation</strong> <strong>of</strong> Di¤usion Models. Econometric Theory26, 541-563.22

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