------------------------------------------------------------------------------- help forbetafit postestimation-------------------------------------------------------------------------------

Title

betafit postestimation-- Postestimation tools for betafit

DescriptionThis file documents postestimation tools for betafit.

dbetafitdisplays discrete changes and marginal effects after betafit.The following standard postestimation commands are also available:

command description ------------------------------------------------------------------------- INCLUDE help post_estat INCLUDE help post_estimates INCLUDE help post_lincom INCLUDE help post_lrtest INCLUDE help post_mfx

marginsmarginal means, predictive margins, marginal effects, and average marginal effects INCLUDE help post_nlcompredictpredictions and residuals INCLUDE help post_predictnl INCLUDE help post_suest INCLUDE help post_test INCLUDE help post_testnl -------------------------------------------------------------------------

------------------------------------------------------------------------------- help for

dbetafit-------------------------------------------------------------------------------

Syntax for dbetafit

dbetafit[,at(variables_and_values)]

Description

dbetafitdisplays changes in predicted dependent variable for three types of discrete changes in explanatory variables and two types of marginal effects:

Discrete changes

Min --> Maxshows the change in predicted dependent variable when an explanatory variable changes from its minimum value to its maximum value, while keeping all other variables at their specified values (by default their means). This is the only effect shown for indicator or dummy variables (variables with only two distinct values).

+-SD/2shows the change in predicted dependent variable when an explanatory variable moves from half a standard deviation below its specified value (by default the mean) to half a standard deviation above its specified value, while keeping all other variables at their specified values. In other words, it shows the effect of a standard deviation change in an explanatory variable, centred on the specified value, on the predicted dependent variable.

+-1/2shows the change in predicted dependent variable when an explanatory variable moves from half a unit below its specified value to half a unit above its specified value, while keeping all other variables at their specified values. In other words, it shows the effect of a unit change in explanatory variable, centred on the specified value, on the predicted dependent variable.

Marginal effects

MFX at xshows the marginal effect of each (non-indicator or non-dummy) variable, while keeping all variables at their specified values. The marginal effect is the change in predicted dependent variable for a unit change in the explanatory variable, assuming that the effect does not change over that interval.

Max MFXis the maximum marginal effect. Marginal effects change depending on which values of the explanatory variables they are evaluated for. The maximum marginal effect shows the marginal effect you would get if you had chosen those values of the explanatory variables that would maximize the marginal effect. The marginal effect is maximum if the predicted proportion is .5; hence this may lie outside the range of the observed data.

dbetafitis only allowed afterbetafitin the alternative parameterization, i.e. if one or bothmuvar()andphivar()is specified or if thealternativeoption is specified.mfxcan be used to get marginal effects afterbetafitin both the conventional and the alternative parametrization.

Options

at(variables_and_values)is used to specify at which values of the explanatory variables the effects are calculated.variables_and_valuesis an alternating list of variables and either numeric values or mean, median, min, max, p1, p5, p10, p25, p50, p75, p90, p95, p99. The default is mean for all variables. The statistics p1, p5, ..., p99, are the 1st, 5th, ..., 99th percentiles.

Examples

use http://fmwww.bc.edu/repec/bocode/c/citybudget.dta, clear

betafit governing, mu(minorityleft noleft houseval popdens)

dbetafit, at(minorityleft 0 noleft 0)(click to run)

Author ofdbetafitMaarten L. Buis, Universitaet Tuebingen maarten.buis@uni-tuebingen.de

------------------------------------------------------------------------------- help for

predict-------------------------------------------------------------------------------

Syntax for predict

predict[type]newvar[if] [in] [,statisticvar(varname)]

statisticDescription -------------------------------------------------------------------------proportionproportion (the default)xbxb, fitted valuesstdpstandard error of the predictionsdstandard deviationalphaalpha in the conventional parameterizationbetabeta in the conventional parameterizationmumu in the alternative parameterization, equivalent toproportionphiphi in the alternative parameterization *pearsonPearson residuals *workingworking residuals *partialpartial residuals *scresidualscore residualsscorefirst derivative of the log likelihood with respect to the linear predictor. ------------------------------------------------------------------------- INCLUDE help unstarred

Options for predict

proportion(the default) calculates the proportions.

xbcalculates the linear prediction.

stdpcalculates the standard error of the linear prediction.

sdcalculates the estimated standard deviation of the depedent variable.

alphacalculates alpha in the conventional parameterization. This can be specified after estimating a model usingbetafit, regardless of the parameterization used.

betacalculates beta in the conventional parameterization. This can be specified after estimating a model usingbetafit, regardless of the parameterization used.

phicalculates phi in the alternative parameterization. This can be specified after estimating a model usingbetafit, regardless of the parameterization used.

pearsoncalculates the Pearson residuals, the raw residuals scaled by the estimated standard deviation of the predicted proportion.

workingcalculates the working residuals as discussed on page 53 of Hardin and Hilbe (2007).

partialcalculates the partial residuals as discussed on page 54 of Hardin and Hilbe (2007). This requires that one of the explanatory variables is specified in thevar()option.

scresidualcalculates the score residuals (see Rocha and Simas (forthcoming)) or "standardized weighted residuals 1" (see Espinheira et al. (2008)). These residuals tend to be more symmetric and normal/Gaussian-like, making them easier to interpret than Pearson residuals.

scorecalculates the first derivative of the log likelihood with respect to the linear predictions.

ReferencesCox, Nicholas J. 2005. Speaking Stata: The protean quantile plot.

TheStata Journal5(3): 442-460. http://www.stata-journal.com/article.html?article=gr0018Espinheira, Patrícia L., Ferrari, Silvia L.P., and Cribari-Neto, Francisco. 2008. On beta regression residuals.

Journal of AppliedStatistics35(4): 407-419.Hardin, James W. and Hilbe, Joseph M. 2007.

Generalized Linear Models andExtensions(2nd edition). College Station, TX: Stata Press.Rocha, Andréa V. and Simas, Alexandre B. (forthcoming). Influence diagnostics in a general class of beta regression models.

Mathematics and Statistics.Winter, Nicholas J.G. 2005. Stata tip 23: Regaining control over axis ranges.

The Stata Journal5(3): 467-468. http://www.stata-journal.com/article.html?article=gr0019

Also seeOnline: help for

betafit,estimates,lincom,lrtest,mfx,nlcom,predict,predictnl,suest,test,testnl