help for gbgfit                                      Austin Nichols (June 2009)

Program to fit a Generalized Beta (Type 2) distribution to grouped data via ML

gbgfit nvar [if exp] [in range] [, z1(z1var) z2(z2var) from(string) avar(var) bvar(var) pvar(var) qvar(var) sva(var) svb(var) svp(var) svq(var) replace double level(#) maximize_options ]

by ... : may be used with gbgfit; see help by.


gbgfit fits by ML the 3 parameter Generalized Beta (Type 2), or GB2, distribution to a sample of counts or frequencies in nvar by income category. (For an estimator designed for unit record data, see gb2fit). Optionally specified variables z1var and z2var encode the lower and upper limits of each interval; if they are not specified, variables serving that role and called z1 and z2 are assumed to exist.

The GB2 distribution seems to provide a good fit to empirical income data relative to other parametric functional forms; see e.g. McDonald (1984). It includes the 3-parameter Singh-Maddala (Burr Type 12) and Dagum (Burr Type 3) distributions as special cases; see Singh-Maddala (1976) and Dagum (1977,1980). The Singh-Maddala distribution is the special case when parameter p = 1, and the Dagum distribution is the special case when parameter q = 1. See also help for dagfit or smgfit if installed (or see dagumfit and smfit for estimators designed for unit record data). The GB2 distribution is also known as the Generalized F distribution (differently parameterized) or the Feller-Pareto distribution (with a nonzero minimum). For a comprehensive review of these and other related distributions, see Kleiber and Kotz (2003). The GB2 distribution may be useful for describing any skewed positive variable.

To test whether a given distribution assumed to be well-modeled as a GB2 distribution can also be usefully modeled as a Singh-Maddala or Dagum distribution, one might want to test the p and q parameters for equality to one, as shown in the example. Since q = 1 approximately (and we cannot reject the null that q = 1) in the estimates shown, it seems reasonable to model the distribution as Dagum. Looking at graphs, we can see how closely the Dagum and GB2 estimates of the p.d.f. correspond in the example.

Example from McDonald (1984)

clear all input z1 f70 f75 f80 0 66 35 21 2.5 125 85 41 5 152 106 62 7.5 166 106 65 10 158 114 73 12.5 110 109 69 15 131 188 140 20 46 116 137 25 30 95 198 35 11 32 128 50 05 14 67 end g z2=z1[_n+1] g mid=(z1+z2)/2 replace mid=100 if mid==. set obs 200 g x=_n/2 la var x "Income" gbgfit f70, difficult test [p]_b[_cons]=1 test [q]_b[_cons]=1 g g=(e(ba)*x^(e(ba)*e(bp)-1))/e(bb)^(e(ba)*e(bp))/(1+(x/e(bb))^e(ba))^(e(bp)+e( > bq))*exp(lngamma(e(bp)+e(bq))-lngamma(e(bp))-lngamma(e(bq))) la var g "PDF for grouped GB2 MLE 1970" gb2fit mid [fw=f70], from(4 20 .3 .6, copy) difficult g g2=(e(ba)*x^(e(ba)*e(bp)-1))/e(bb)^(e(ba)*e(bp))/(1+(x/e(bb))^e(ba))^(e(bp)+e > (bq))*exp(lngamma(e(bp)+e(bq))-lngamma(e(bp))-lngamma(e(bq))) la var g2 "PDF for indiv. GB2 MLE applied to group data from 1970" dagfit f70 g dg=e(ba)*e(bp)*(e(bb)/x)^e(ba)/x*(1+(e(bb)/x)^e(ba))^(-e(bp)-1) la var dg "PDF for grouped Dagum MLE 1970" smgfit f70 g sg=(e(ba)*e(bq)/e(bb))*((1+(x/e(bb))^e(ba))^-(e(bq)+1))*((x/e(bb))^(e(ba)-1)) la var sg "PDF for grouped Singh-Maddala MLE 1970" tw hist mid [fw=f70]||line g x, name(hist) line g x||line g2 x||line dg x||line sg x, leg(col(1)) g dd=g-dg g ds=g-sg la var dd "Diff. betw. GB2 and Dagum PDF" la var ds "Diff. betw. GB2 and S-M PDF" line dd ds x, leg(col(1)) name(dpdf)


z1(z1var), z2(z2var) are the lower and upper limits of each interval; if they are not specified, variables serving that role and called z1 and z2 are assumed to exist. It should always be true that the upper bound of one category is the same as the lower bound of the next highest category.

?var(var) options specify varlists that are assumed to have a linear effect on the parameter specified. By default, each varlist contains only a constant, so only the parameter itself is estimated.

sv?(var) options specify newvars in which to store the estimated parameters. If ?var(var) options have been specified, the prediction assumes all explanatory variables are zero, i.e. the prediction is only for the constant.

replace allows sv?(var) options to specify existing variables, whose values are replaced in the estimation sample.

double requests that sv?(var) create doubles.

from(string) specifies initial values for the parameters, and it may be useful to try different starting parameters to assess the dependence of estimates on starting values (the generalized beta is more sensitive to initial parameter vectors than the Dagum or Singh-Maddala), or to aid convergence speed. You can specify the initial values in one of three ways: the name of a vector containing the initial values (e.g., from(b0) where b0 is a properly labeled vector); by specifying coefficient names with the values (e.g., from(a:_cons=1 b:_cons=5 p:_cons = 1 q:_cons = 1); or by specifying an ordered list of values (e.g., from(1 5 1 1, copy)). Poor values in from() may lead to convergence problems. For more details, including the use of copy and skip, see {help:maximize}.

level(#) specifies the confidence level, in percent, for the confidence intervals of the coefficients; see help level.

nolog suppresses the iteration log.

maximize_options control the maximization process. The options available are those shown by maximize, with the exception of from(). If you are seeing many "(not concave)" messages in the iteration log, using the difficult or technique options may help convergence.

Saved results

In addition to the usual results saved after ml, dagfit also saves the following:

e(a), e(b), e(p), and e(q) are the estimated GB2 parameters. If covariates are specified, these are the parameters when all covariates are zero; i.e. these are the constant terms in each equation. These parameters are used to calculate e(mode), e(mean), e(var), e(sd), e(i2), and e(gini), which are the estimated mode, mean, variance, standard deviation, half coefficient of variation squared, and Gini coefficient, respectively. e(pX), and e(LpX) are the quantiles, and Lorenz ordinates, where X ranges from 1 to 99.


The GB2 distribution has distribution function (c.d.f.)

F(x) = ibeta(p, q, (x/b)^a/(1+(x/b)^a) )

where a, b, p, q, are parameters, each positive, for random variable x > 0. Parameters a, p, and q are the key distributional 'shape' parameters; b is a scale parameter.

The GB2 distribution has density

f(x) = ax^(ap-1)*{(b^(a*p))*B(p,q)*[1 + (x/b)^a ]^(p+q)}^-1.

The likelihood function for a sample of observations on nvar is specified as the product of the density integrated from z1 to z2 and raised to the power nvar, the count of observations in the category, and is maximized using ml model lf.

The formulae used to derive the distributional summary statistics presented (optionally) are as follows. The r-th moment about the origin is given by


where B(u,v) is the Beta function = G(u)*G(v)/G(u+v) and G(.)=exp(lngamma(.)) is the gamma function. The moments exist for -ap < r < aq. By substitution and using G(1) = 1, the moments can be written


and hence

mean = b*G(p+1/a)*G(q-1/a)/[G(p)G(q)]

variance = b*b*G(p+2/a)*G(q-2/a)/[G(p)G(q)] - (mean^2)

from which the standard deviation and half the squared coefficient of variation can be derived. The mode is

mode = b*((ap-1)/(aq+1))^(1/a) if ap > 1, and 0 otherwise.

The quantiles are derived by inverting the distribution function, and calculation of the Lorenz ordinates exploits the fact that they follow a GB2 distribution; see Kleiber and Kotz, 2003, eqn (6.23). The Gini coefficient is not calculated as this requires evaluation of the generalized hypergeometric 3F2, and this function is not currently available in Stata. Online evaluators are available, at e.g. wolfram.com, where you can plug in specific parameter values to calculate the generalized hypergeometric 3F2, then use the formula given by McDonald (1984) to calculate the Gini.


Austin Nichols <austinnichols@gmail.com>


Stephen P. Jenkins has made available commands for fitting various distributions (including the GB2) to individual record data; see Jenkins (2004). This package draws liberally from that work; note in particular the similarity of verbiage under headings Formulae and Description.


Dagum, C. (1977). A new model of personal income distribution: specification and estimation. Economie Appliquée 30: 413-437.

Dagum, C. (1980). The generation and distribution of income, the Lorenz curve and the Gini ratio. Economie Appliquée 33: 327-367.

Jenkins, S.P. (2004). Fitting functional forms to distributions, using ml. Presentation at Second German Stata Users Group Meeting, Berlin. http://www.stata.com/meeting/2german/Jenkins.pdf

Kleiber, C. (1996). Dagum vs. Singh-Maddala income distributions. Economics Letters 53: 265-268. http://dx.doi.org/10.1016/S0165-1765(96)00937-8

Kleiber, C. and Kotz, S. (2003). Statistical Size Distributions in Economics and Actuarial Sciences. Hoboken, NJ: John Wiley.

McDonald, J.B. (1984). Some generalized functions for the size distribution of income. Econometrica 52: 647-663. http://www.jstor.org/stable/1913469

Singh, S.K. and G.S. Maddala (1976). A function for the size distribution of income. Econometrica 44: 963-970.

Also see

Online: help for dagfit, smgfit, dagumfit, smfit, gb2fit, lognfit, if installed, or acquire them from ssc.