4 oecdpaneloecdpanelCross Country Growth PanelDescriptionCross country GDP growth panel covering the period 1960-1995 used by Liu and Stengos (2000)and Maasoumi, Racine, and Stengos (2007). <strong>The</strong>re are 616 observations in total. data("oecdpanel")makes available the dataset "oecdpanel" plus an additional object "bw".Usagedata("oecdpanel")FormatA data frame with 7 columns, and 616 rows. This panel covers 7 5-year periods: 1960-1964, 1965-1969, 1970-1974, 1975-1979, 1980-1984, 1985-1989 and 1990-1994.A separate local-linear rbandwidth object (‘bw’) has been computed for the user’s conveniencewhich can be used to visualize this dataset using <strong>np</strong>plot(bws=bw).growth the first column, of type numeric: growth rate of real GDP per capita for each 5-yearperiodoecd the second column, of type integer: equal to 1 for OECD members, 0 otherwiseyear the third column, of type integerinitgdp the fourth column, of type numeric: per capita real GDP at the beginning of each 5-yearperiodpopgro the fifth column, of type numeric: average annual population growth rate for each 5-yearperiodinv the sixth column, of type numeric: average investment/GDP ratio for each 5-year periodhumancap the seventh column, of type numeric: average secondary school enrollment rate foreach 5-year period<strong>Source</strong>Thanasis StengosReferencesLiu, Z. and T. Stengos (1999), “Non-linearities in cross country growth regressions: a semiparametricapproach,” Journal of Applied Econometrics, 14, 527-538.Maasoumi, E. and J.S. Racine and T. Stengos (2007), “Growth and convergence: a profile of distributiondynamics and mobility,” Journal of Econometrics, 136, 483-508
wage1 5Examplesdata("oecdpanel")attach(oecdpanel)summary(oecdpanel)detach(oecdpanel)wage1Cross-Sectional Data on WagesDescriptionUsageFormatCross-section wage data consisting of a random sample taken from the U.S. Current PopulationSurvey for the year 1976. <strong>The</strong>re are 526 observations in total. data("wage1") makes availablethe dataset "wage" plus additional objects "bw.all" and "bw.subset".data("wage1")A data frame with 24 columns, and 526 rows.Two local-linear rbandwidth objects (‘bw.all’ and ‘bw.subset’) have been computed forthe user’s convenience which can be used to visualize this dataset using <strong>np</strong>plot(bws=bw.all)wage column 1, of type numeric, average hourly earningseduc column 2, of type numeric, years of educationexper column 3, of type numeric, years potential experiencetenure column 4, of type numeric, years with current employernonwhite column 5, of type character, =“Nonwhite” if nonwhite, “White” otherwisefemale column 6, of type character, =“Female” if female, “Male” otherwisemarried column 7, of type character, =“Married” if Married, “Nonmarried” otherwisenumdep column 8, of type numeric, number of dependentssmsa column 9, of type numeric, =1 if live in SMSAnorthcen column 10, of type numeric, =1 if live in north central U.Ssouth column 11, of type numeric, =1 if live in southern regionwest column 12, of type numeric, =1 if live in western regionconstruc column 13, of type numeric, =1 if work in construc. indus.ndurman column 14, of type numeric, =1 if in nondur. manuf. indus.trcommpu column 15, of type numeric, =1 if in trans, commun, pub uttrade column 16, of type numeric, =1 if in wholesale or retailservices column 17, of type numeric, =1 if in services indus.
- Page 1 and 2: The np PackageFebruary 16, 2008Vers
- Page 3: Italy 3Examplesdata("cps71")attach(
- Page 7 and 8: gradients 7## S3 method for class '
- Page 9 and 10: np 9A variety of bandwidth methods
- Page 11 and 12: npcmstest 11npcmstestKernel Consist
- Page 13 and 14: npcmstest 13ReferencesAitchison, J.
- Page 15 and 16: npcdens 15npcdensKernel Conditional
- Page 17 and 18: npcdens 17Valuenpcdens returns a co
- Page 19 and 20: npcdens 19# Gaussian kernel (defaul
- Page 21 and 22: npcdens 21# (1993) (see their descr
- Page 23 and 24: npcdensbw 23fit
- Page 25 and 26: npcdensbw 25na.actionxdatydatbwspro
- Page 27 and 28: npcdensbw 27data. The approach is b
- Page 29 and 30: npconmode 29# depending on the spee
- Page 31 and 32: npconmode 31tydatexdateydata one (1
- Page 33 and 34: npconmode 33lwt,family=binomial(lin
- Page 35 and 36: npudens 35npudensKernel Density and
- Page 37 and 38: npudens 37Author(s)Tristen Hayfield
- Page 39 and 40: npudens 39# EXAMPLE 1 (INTERFACE=DA
- Page 41 and 42: npudensbw 41library("datasets")data
- Page 43 and 44: npudensbw 43bwsa bandwidth specific
- Page 45 and 46: npudensbw 45fvalobjective function
- Page 47 and 48: npudensbw 47# previous examples.bw
- Page 49 and 50: npudensbw 49# previous examples.bw
- Page 51 and 52: npksum 51Argumentsformuladatanewdat
- Page 53 and 54: npksum 53Usage IssuesIf you are usi
- Page 55 and 56:
npksum 55# the bandwidth object its
- Page 57 and 58:
npksum 57ss
- Page 59 and 60:
npplot 59plot.behavior = c("plot","
- Page 61 and 62:
npplot 61xtrim = 0.0,neval = 50,com
- Page 63 and 64:
npplot 63xdatydatzdatxqyqzqxtrimytr
- Page 65 and 66:
npplot 65DetailsValuenpplot is a ge
- Page 67 and 68:
npplot 67year.seq
- Page 69 and 70:
npplot 69# npplot(). When npplot()
- Page 71 and 72:
npplreg 71## S3 method for class 'c
- Page 73 and 74:
npplreg 73residR2MSEMAEMAPECORRSIGN
- Page 75 and 76:
npplreg 75# Plot the regression sur
- Page 77 and 78:
npplregbw 77and dependent data), an
- Page 79 and 80:
npplregbw 79Detailsnpplregbw implem
- Page 81 and 82:
npplregbw 81x2
- Page 83 and 84:
npqcmstest 83npqcmstestKernel Consi
- Page 85 and 86:
npqcmstest 85Author(s)Tristen Hayfi
- Page 87 and 88:
npqreg 87ftol = 1.19209e-07,tol = 1
- Page 89 and 90:
npqreg 89Li, Q. and J.S. Racine (20
- Page 91 and 92:
npreg 91Usagenpreg(bws, ...)## S3 m
- Page 93 and 94:
npreg 93residR2MSEMAEMAPECORRSIGNif
- Page 95 and 96:
npreg 95summary(model)# Use npplot(
- Page 97 and 98:
npreg 97# - this may take a few min
- Page 99 and 100:
npreg 99# then a noisy samplen
- Page 101 and 102:
npregbw 101## S3 method for class '
- Page 103 and 104:
npregbw 103ckerorderukertypeokertyp
- Page 105 and 106:
npregbw 105ReferencesAitchison, J.
- Page 107 and 108:
npsigtest 107bw
- Page 109 and 110:
npsigtest 109Author(s)Tristen Hayfi
- Page 111 and 112:
npindex 111Usagenpindex(bws, ...)##
- Page 113 and 114:
npindex 113MAEMAPECORRSIGNif method
- Page 115 and 116:
npindex 115x2
- Page 117 and 118:
npindex 117# x1 is chi-squared havi
- Page 119 and 120:
npindexbw 119# plotting via persp()
- Page 121 and 122:
npindexbw 121methodnmultithe single
- Page 123 and 124:
npindexbw 123allows one to deploy t
- Page 125 and 126:
npscoef 125x1
- Page 127 and 128:
npscoef 127Valueeydatezdaterrorsres
- Page 129 and 130:
npscoef 129# We could manually plot
- Page 131 and 132:
npscoefbw 131optim.abstol,optim.max
- Page 133 and 134:
npscoefbw 133optim.maxattemptsmaxim
- Page 135 and 136:
npscoefbw 135ReferencesAitchison, J
- Page 137 and 138:
uocquantile 137uocquantileCompute Q
- Page 139:
INDEX 139npconmode, 29npindex, 110n