88 <strong>np</strong>qreggradientsitmaxftoltolsmalla logical value indicating that you want gradients computed and returned in theresulting <strong>np</strong>regression object. Defaults to FALSE.integer number of iterations before failure in the numerical optimization routine.Defaults to 10000.tolerance on the value of the objective function evaluated at located minima.Defaults to 1.19e-07 (FLT_EPSILON).tolerance on the position of located minima of the objective function. Defaultsto 1.49e-08 (sqrt(DBL_EPSILON)).a small number, at about the precision of the data type used. Defaults to 2.22e-16 (DBL_EPSILON).Value<strong>np</strong>qreg returns a <strong>np</strong>qregression object. <strong>The</strong> generic functions fitted (or quantile), se,predict, and gradients, extract (or generate) estimated values, asymptotic standard errorson estimates, predictions, and gradients, respectively, from the returned object. Furthermore, thefunctions summary and plot support objects of this type. <strong>The</strong> returned object has the followingcomponents:evalquantilequanterrquantgradtauevaluation pointsestimation of the quantile regression function (conditional quantile) at the evaluatio<strong>np</strong>ointsstandard errors of the quantile regression estimatesgradients at each evaluation pointthe τth quantile computedUsage IssuesIf you are using data of mixed types, then it is advisable to use the data.frame function toconstruct your i<strong>np</strong>ut data and not cbind, since cbind will typically not work as intended onmixed data types and will coerce the data to the same type.Author(s)Tristen Hayfield 〈hayfield@phys.ethz.ch〉, Jeffrey S. Racine 〈racinej@mcmaster.ca〉ReferencesAitchison, J. and C.G.G. Aitken (1976), “Multivariate binary discrimination by the kernel method,”Biometrika, 63, 413-420.Hall, P. and J.S. Racine and Q. Li (2004), “Cross-validation and the estimation of conditional probabilitydensities,” Journal of the American Statistical Association, 99, 1015-1026.Koenker, R. W. and G.W. Bassett (1978), “Regression quantiles,” Econometrica, 46, 33-50.Koenker, R. (2005), Quantile Regression, Econometric Society Monograph Series, Cambridge UniversityPress.
<strong>np</strong>qreg 89Li, Q. and J.S. Racine (2007), No<strong>np</strong>arametric Econometrics: <strong>The</strong>ory and Practice, Princeton UniversityPress.Li, Q. and J.S. Racine (forthcoming), “No<strong>np</strong>arametric estimation of conditional CDF and quantilefunctions with mixed categorical and continuous data,” Journal of Business and Economic Statistics.Wang, M.C. and J. van Ryzin (1981), “A class of smooth estimators for discrete distributions,”Biometrika, 68, 301-309.See AlsoquantregExamples# EXAMPLE 1 (INTERFACE=FORMULA): For this example, we compute a# bivariate no<strong>np</strong>arametric quantile regression estimate for Giovanni# Baiocchi's Italian income panel (see Italy for details)data("Italy")attach(Italy)# First, compute the likelihood cross-validation bandwidths (default). We# override the default tolerances for the search method as the objective# function is well-behaved (don't of course do this in general). Note -# this may take a few minutes depending on the speed of your computer...bw
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npcmstest 11npcmstestKernel Consist
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npcmstest 13ReferencesAitchison, J.
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npcdens 15npcdensKernel Conditional
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npcdens 17Valuenpcdens returns a co
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npcdens 19# Gaussian kernel (defaul
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npcdens 21# (1993) (see their descr
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npcdensbw 23fit
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npcdensbw 25na.actionxdatydatbwspro
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npcdensbw 27data. The approach is b
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npconmode 29# depending on the spee
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npconmode 31tydatexdateydata one (1
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npconmode 33lwt,family=binomial(lin
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npudens 35npudensKernel Density and
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- Page 109 and 110: npsigtest 109Author(s)Tristen Hayfi
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- Page 135 and 136: npscoefbw 135ReferencesAitchison, J
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INDEX 139npconmode, 29npindex, 110n