A Smooth Nonparametric, Multivariate, Mixed-Data Location-Scale Test

Jeffrey Racine and Ingrid Van Keilegom ()

Department of Economics Working Papers from McMaster University

Abstract: A number of tests have been proposed for assessing the location-scale assumption that is often invoked by practitioners. Existing approaches include Kolmogorov-Smirnov and Cramer-von-Mises statistics that each involve measures of divergence between unknown joint distribution functions and products of marginal distributions. In practice, the unknown distribution functions embedded in these statistics are approximated using non-smooth empirical distribution functions. We demonstrate how replacing the non-smooth distributions with their kernel-smoothed counter-parts can lead to substantial power improvements. In so doing we extend existing approaches to the smooth multivariate and mixed continuous and discrete data setting thereby extending the reach of existing approaches. Theoretical underpinnings are provided, Monte Carlo simulations are undertaken to assess finite-sample performance, and illustrative applications are provided.

Keywords: Kernel Smoothing; Kolmogorov-Smirnov; Inference (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2017-08-16
New Economics Papers: this item is included in nep-ecm
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http://socserv.mcmaster.ca/econ/rsrch/papers/archive/McMasterEconWP2017-13.pdf (application/pdf)

Related works:
Working Paper: A Smooth Nonparametric, Multivariate, Mixed-Data Location-Scale Test (2019)
Working Paper: A smooth nonparametric, multivariate, mixed-data location-scale test (2019)
Working Paper: A Smooth Nonparametric, Multivariate, Mixed-Data Location-Scale Test (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:mcm:deptwp:2017-13

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