The name is absent

Syntax

dagumfit incvar ^veight] [if exp∖ [in range] [, stats cdîOcdfname) pdf (pdfaame)
level(#) nolog trace b0(#) d0(#) h0(#)]

fweights and aweights are allowed.

To reset problem-size limits, see help matsize.

Options

stats displays selected distributional statistics implied by the Dagum model parameter estimates: percentiles, cumulative shares
of total income at percentiles (i.e., the Lorenz curve ordinates), the mean, standard deviation, variance, half the coefficient
of variation squared, Gini coefficient, and percentile ratios p90∕p10, p75∕p25.

cdf Cdffaame) creates a new variable cdfname containing the estimated Dagum cdf value F (ж) for each x.

pd±(pdfname) creates a new variable pdfname containing the estimated Dagum pdf value /(ж) for each ж.

level(#) specifies the confidence level, in percent, for confidence intervals. The default is level(95) or as set by set level;
see [U] 26.4 Specifying the width of confidence intervals.

nolog suppresses the iteration logs.

trace reports the current value of the estimated parameters at each iteration. See [R] maximize.

bO(#), d0(#), h0(#) allow the user to specify starting values for the Dagumparameters. Default starting values are b = exp(4),
0 = exp(0.1), and h = 1 + exp(13).

Saved results

The global macros set by ml post, plus

S_b, S_d, S_h estimated parameters 6, d, h, respectively

Access to estimated coefficients (transformations of the parameters) and their standard errors are available in the usual way;
see [U] 20.5 Accessing coefficients and standard errors, and [R] matrix get.

Examples

The illustrative examples use the same income distribution data as described in Jenkins (1999). The income variable is
eybhc with fweight variable wgt.

In order to compare the results of smfit and dagumfit, the former is run excluding nonpositive values of eybhc. The
Singh-Maddala distribution is defined for nonnegative incomes but the Dagum distribution only for positive incomes. The results
are as follows:

. smfit eybhc [fw = wgt] if eybhc>0_t stats cdf(smF) pdf(smf)

Iteration 0: Log Likelihood = -40547.317

Iteration 1: Log Likelihood = -40062.416

Iteration 2: Log Likelihood = -39888.368

Iteration 3: Log Likelihood = -39879.841

Iteration 4: Log Likelihood = -39879.785

Iteration 5: Log Likelihood = -39879.785

ML fit of Singh-Maddala distribution                Number of obs =    6448

Model chi2(0)

Prob > chi2 =

Log Likelihood = -39879.7845655

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