{smcl} {hline} {cmd:help: {helpb logithetm}}{space 55} {cmd:dialog:} {bf:{dialog logithetm}} {hline} {bf:{err:{dlgtab:Title}}} {bf: logithetm: Logit Multiplicative Heteroscedasticity Regression} {bf:{err:{dlgtab:Syntax}}} {p 8 16 2} {cmd:logithetm} {depvar} [{indepvars}] {ifin} {weight} {cmd:,} {cmd:het(}{it:varlist} [{cmd:,} {opt off:set(varname)}]{cmd:)} [{it:options}] {synoptset 27 tabbed}{...} {synopthdr} {synoptline} {syntab :Model} {p2coldent :* {cmd:het(}{varlist}[...]{cmd:)}}independent variables to model the variance and possible offset variable{p_end} {synopt :{opt nocon:stant}}suppress constant term{p_end} {synopt :{opth off:set(varname)}}include {it:varname} in model with coefficient constrained to 1{p_end} {synopt :{opt asis}}retain perfect predictor variables{p_end} {synopt :{cmdab:const:raints(}{it:{help estimation options##constraints():constraints}}{cmd:)}}apply specified linear constraints{p_end} {synopt:{opt col:linear}}keep collinear variables{p_end} {syntab :SE/Robust} {synopt :{opth vce(vcetype)}}{it:vcetype} may be {opt oim}, {opt r:obust}, {opt cl:uster} {it:clustvar}, {cmd:opg}, {opt boot:strap}, or {opt jack:knife}{p_end} {syntab :Reporting} {synopt :{opt l:evel(#)}}set confidence level; default is {cmd:level(95)}{p_end} {synopt :{opt noskip}}perform likelihood-ratio test{p_end} {synopt :{opt nolr:test}}perform Wald test on variance{p_end} {synopt :{opt nocnsr:eport}}do not display constraints{p_end} {synopt :{it:{help logithetm##display_options:display_options}}}control spacing and display of omitted variables and base and empty cells{p_end} {syntab :Maximization} {synopt :{it:{help logithetm##maximize_options:maximize_options}}}control the maximization process; seldom used{p_end} {p2coldent:+ {opt coefl:egend}}display coefficients' legend instead of coefficient table{p_end} {synoptline} {p2colreset}{...} {p 4 6 2} * {opt het()} is required. The full specification is{break} {cmd:het(}{it:varlist} [{cmd:,} {opt off:set(varname)}]{cmd:)} {p_end} {p 4 6 2} + {opt coeflegend} does not appear in the dialog box.{p_end} INCLUDE help fvvarlist2 {p 4 6 2}{it:depvar}, {it:indepvars}, and {it:varlist} may contain time-series operators; see {help tsvarlist}.{p_end} {p 4 6 2}{cmd:bootstrap}, {cmd:by}, {cmd:jackknife}, {cmd:rolling}, {cmd:statsby}, and {cmd:svy} are allowed; see {help prefix}.{p_end} {p 4 6 2}Weights are not allowed with the {helpb bootstrap} prefix.{p_end} {p 4 6 2} {opt vce()}, {opt noskip}, and weights are not allowed with the {helpb svy} prefix.{p_end} {p 4 6 2}{opt fweight}s, {opt iweight}s, and {opt pweight}s are allowed; see {help weight}.{p_end} {bf:{err:{dlgtab:Description}}} {pstd} {cmd:logithetm} fits a maximum-likelihood for Logit Multiplicative Heteroscedasticity Regression. {bf:{err:{dlgtab:Options}}} {dlgtab:Model} {phang} {cmd:het(}{it:varlist} [{cmd:,} {opt offset(varname)}]{cmd:)} specifies the independent variables and the offset variable, if there is one, in the variance function. {opt het()} is required. {phang} {opt noconstant}, {opth offset(varname)}; see {helpb estimation options:[R] estimation options}. {phang} {opt asis} forces the retention of perfect predictor variables and their associated perfectly predicted observations and may produce instabilities in maximization; see {manhelp logit R}. {phang} {opt constraints(constraints)}, {opt collinear}; see {helpb estimation options:[R] estimation options}. {dlgtab:SE/Robust} INCLUDE help vce_asymptall {dlgtab:Reporting} {phang} {opt level(#)}; see {helpb estimation options##level():[R] estimation options}. {phang} {opt noskip} requests fitting of the constant-only model and calculation of the corresponding likelihood-ratio chi-squared statistic for testing significance of the full model. By default, a Wald chi-squared statistic is computed for testing the significance of the full model. {phang} {opt nolrtest} specifies that a Wald test of whether {cmd:lnsigma2} = 0 be performed instead of the LR test. {phang} {opt nocnsreport}; see {helpb estimation options##nocnsreport:[R] estimation options}. {marker display_options}{...} {phang} {it:display_options}: {opt noomit:ted}, {opt vsquish}, {opt noempty:cells}, {opt base:levels}, {opt allbase:levels}; see {helpb estimation options##display_options:[R] estimation options}. {marker maximize_options}{...} {dlgtab :Maximization} {phang} {it:maximize_options}: {opt dif:ficult}, {opt tech:nique(algorithm_spec)}, {opt iter:ate(#)}, [{cmdab:no:}]{opt lo:g}, {opt tr:ace}, {opt grad:ient}, {opt showstep}, {opt hess:ian}, {opt showtol:erance}, {opt tol:erance(#)}, {opt ltol:erance(#)}, {opt nrtol:erance(#)}, {opt nonrtol:erance}, {opt from(init_specs)}; see {manhelp maximize R}. These options are seldom used. {pmore} Setting the optimization type to {cmd:technique(bhhh)} resets the default {it:vcetype} to {cmd:vce(opg)}. {pstd} The following option is available with {opt logithetm} but is not shown in the dialog box: {phang} {opt coeflegend}; see {helpb estimation options##coeflegend:[R] estimation options}. {bf:{err:{dlgtab:Saved Results}}} {pstd} {cmd:logithetm} saves the following in {cmd:e()}: {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Scalars}{p_end} {synopt:{cmd:e(N)}}number of observations{p_end} {synopt:{cmd:e(N_f)}}number of zero outcomes{p_end} {synopt:{cmd:e(N_s)}}number of nonzero outcomes{p_end} {synopt:{cmd:e(k)}}number of parameters{p_end} {synopt:{cmd:e(k_eq)}}number of equations{p_end} {synopt:{cmd:e(k_eq_model)}}number of equations in model Wald test{p_end} {synopt:{cmd:e(k_dv)}}number of dependent variables{p_end} {synopt:{cmd:e(k_autoCns)}}number of base, empty, and omitted constraints{p_end} {synopt:{cmd:e(df_m)}}model degrees of freedom{p_end} {synopt:{cmd:e(ll)}}log likelihood{p_end} {synopt:{cmd:e(chi2)}}chi-squared{p_end} {synopt:{cmd:e(chi2_c)}}chi-squared for heteroskedasticity LR test{p_end} {synopt:{cmd:e(p)}}significance{p_end} {synopt:{cmd:e(rank)}}rank of {cmd:e(V)}{p_end} {synopt:{cmd:e(ic)}}number of iterations{p_end} {synopt:{cmd:e(rc)}}return code{p_end} {synopt:{cmd:e(converged)}}{cmd:1} if converged, {cmd:0} otherwise{p_end} {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Macros}{p_end} {synopt:{cmd:e(cmd)}}{cmd:logithetm}{p_end} {synopt:{cmd:e(cmdline)}}command as typed{p_end} {synopt:{cmd:e(depvar)}}name of dependent variable{p_end} {synopt:{cmd:e(wtype)}}weight type{p_end} {synopt:{cmd:e(wexp)}}weight expression{p_end} {synopt:{cmd:e(title)}}title in estimation output{p_end} {synopt:{cmd:e(clustvar)}}name of cluster variable{p_end} {synopt:{cmd:e(offset1)}}offset for Logit equation{p_end} {synopt:{cmd:e(offset2)}}offset for variance equation{p_end} {synopt:{cmd:e(chi2type)}}{cmd:Wald} or {cmd:LR}; type of model chi-squared test{p_end} {synopt:{cmd:e(chi2_ct)}}{cmd:Wald} or {cmd:LR}; type of model chi-squared test corresponding to {cmd:e(chi2_c)}{p_end} {synopt:{cmd:e(vce)}}{it:vcetype} specified in {cmd:vce()}{p_end} {synopt:{cmd:e(vcetype)}}title used to label Std. Err.{p_end} {synopt:{cmd:e(opt)}}type of optimization{p_end} {synopt:{cmd:e(which)}}{cmd:max} or {cmd:min}; whether optimizer is to perform maximization or minimization{p_end} {synopt:{cmd:e(method)}}requested estimation method{p_end} {synopt:{cmd:e(ml_method)}}type of {cmd:ml} method{p_end} {synopt:{cmd:e(user)}}name of likelihood-evaluator program{p_end} {synopt:{cmd:e(technique)}}maximization technique{p_end} {synopt:{cmd:e(singularHmethod)}}{cmd:m-marquardt} or {cmd:hybrid}; method used when Hessian is singular{p_end} {synopt:{cmd:e(crittype)}}optimization criterion{p_end} {synopt:{cmd:e(properties)}}{cmd:b V}{p_end} {synopt:{cmd:e(predict)}}program used to implement {cmd:predict}{p_end} {synopt:{cmd:e(asbalanced)}}factor variables {cmd:fvset} as {cmd:asbalanced}{p_end} {synopt:{cmd:e(asobserved)}}factor variables {cmd:fvset} as {cmd:asobserved}{p_end} {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Matrices}{p_end} {synopt:{cmd:e(b)}}coefficient vector{p_end} {synopt:{cmd:e(Cns)}}constraints matrix{p_end} {synopt:{cmd:e(ilog)}}iteration log (up to 20 iterations){p_end} {synopt:{cmd:e(gradient)}}gradient vector{p_end} {synopt:{cmd:e(V)}}variance-covariance matrix of the estimators{p_end} {synopt:{cmd:e(V_modelbased)}}model-based variance{p_end} {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Functions}{p_end} {synopt:{cmd:e(sample)}}marks estimation sample{p_end} {p2colreset}{...} {bf:{err:{dlgtab:Examples}}} {stata clear all} {stata sysuse logithetm.dta, clear} {stata db logithetm} {stata logithetm y x1 , het(x1)} {bf:{err:{dlgtab:References}}} {p 4 8 2}Greene, William (1993) {cmd: "Econometric Analysis",} {it:2nd ed., Macmillan Publishing Company Inc., New York, USA.}. {bf:{err:{dlgtab:Author}}} {hi:Emad Abd Elmessih Shehata} {hi:Assistant Professor} {hi:Agricultural Research Center - Agricultural Economics Research Institute - Egypt} {hi:Email: {browse "mailto:emadstat@hotmail.com":emadstat@hotmail.com}} {hi:WebPage:{col 27}{browse "http://emadstat.110mb.com/stata.htm"}} {hi:WebPage at IDEAS:{col 27}{browse "http://ideas.repec.org/f/psh494.html"}} {hi:WebPage at EconPapers:{col 27}{browse "http://econpapers.repec.org/RAS/psh494.htm"}} {bf:{err:{dlgtab:logithetm Citation}}} {phang}Shehata, Emad Abd Elmessih (2011){p_end} {phang}{cmd:logithetm: "Stata Module to Estimate Logit Multiplicative Heteroscedasticity Regression"}{p_end} {browse "http://ideas.repec.org/c/boc/bocode/s457324.html"} {browse "http://econpapers.repec.org/software/bocbocode/s457324.htm"} {psee} {p_end}