We illustrate the usage of the randregret command using stated choice data on route preferences. The command offers robust and cluster standard-error correction using analytical expressions of the score functions. It also offers likelihood ratio tests, which can be used to assess the relevance of a given model specification. Finally, predicted probabilities from each model can be easily computed using the randregretpred postestimation command.

References:

Chorus, C. G. 2010. A new model of random regret minimization. European Journal of Transport and Infrastructure Research 10(2).

Chorus, C. G. 2014. A generalized random regret minimization model. Transportation Research Part B: Methodological 68: 224–238.

Van Cranenburgh, S., C. A. Guevara, and C. G. Chorus. 2015. New insights on random regret minimization models. Transportation Research Part A: Policy and Practice 74: 91–109.

intraclass correlation (ICC) of the dependent variable for the actual sample to assess the maximum of the contextual effect, too. Since the early 90s, a lot of Monte Carlo simulation studies (Hagle and Mitchell 1992; Veall and Zimmermann 1992, 1993, 1994); Windmeijer 1995; DeMaris 2002) proved that McKelvey and Zavoina's pseudo-R² is the best one to assess the fit of binary and ordinal logit models. My ado-file calculates this fit measure in two complementary ways: for the fixed effects only and for the fixed and random effects together. The estimation of McFadden's pseudo-R² uses two different zero models: the random-intercept-only model (RIOM) knowing the contextual units and the fixed-intercept-only model (FIOM) ignoring the contextual units completely. For each of them, it calculates the global likelihood-ratio χ² test statistic 1 whether all fixed effects or all fixed and random effects are zero in the population. It performs two global likelihood-ratio χ² tests for all fixed effects and all fixed and random effects using a probability weight for each level. An empirical study of drug consumption in European countries demonstrates the usefulness of my fit_meologit_2lev.ado or fit_meologit_3lev.ado files for multilevel binary and ordinal logit models.

The command includes the following two hypothesis tests. First, a test of the null hypothesis of no breaks against a user-specified number of breaks. Second, a test of the null hypothesis of k numbers of breaks against the alternative of k+1 number of breaks. Once the number of breaks has been specified, the command allows the estimation of the break dates and the construction of confidence intervals for these dates. The syntax and options of xtbreak are explained. Examples for the estimation of the breakpoints and testing those are given.

each other: we impose no restrictions so that our approach can be used with a wide range of data. In particular, this command allows the estimation of OLS and 2SLS coefficient correcting standard errors across units in three environments: in a spatial setting using objects' coordinates or distance between units, in a network setting starting from the adjacency matrix, and in a multi-way cluster framework taking multiple clustering variables as input. This command suits both cross-sectional and panel databases. Distance and time cutoffs can be specified by the user and linear decay in time and space are also optional.

command called reldistcan be used to estimate and plot the relative density function (relative PDF), a histogram of the relative distribution, or the relative distribution function (relative CDF), or to estimate polarization indices and descriptive statistics of the relative data. The command also supports decomposition of the relative distribution by adjusting differences in location, scale, and shape or differences in covariate distributions (using reweighting). Standard errors are estimated based on influence functions and support complex survey data.

estimated. Second, the commands provide analytical estimates of the variance–covariance matrix of the coefficients for several regressions allowing for weights, clustering, and stratification. Third, in addition to traditional pointwise confidence intervals, these commands also provide functional confidence bands and tests of functional hypotheses. Fourth, predict—called after either qrprocess or drprocess—can generate monotone estimates of the conditional quantile and distribution functions obtained by rearrangement. Fifth, plotprocess conveniently plots the estimated coefficients with their confidence intervals and uniform bands.

vary with covariates; for example, assuming normality and allowing both the mean and the standard deviation to vary with a vector of explanatory variables. Drawing on examples from income distribution analyses, I show how such models are fit in Stata, relying on a range of community-contributed commands, and how predictions and counterfactual simulations for both the conditional and the marginalized (unconditional) distributions can be easily obtained from the fitted models. I also discuss the pros and cons of such conditional likelihood models in comparisons with quantile regression or distribution regression approaches.

and slightly adapted by Bob Rudis for the R platform (cinc.rud.is/w eb/packages/ggeconodist). The package presented here reproduces the look of Rudis' graphs, with the motivation of creating an easily legible and visually appealing data summary.

No prior knowledge of Bayesian analysis is necessary. Some specific topics I will cover are the relationship between likelihood functions, prior distributions, and posterior distributions; Markov chain Monte Carlo (MCMC) using the Metropolis—Hastings algorithm; and how to use Stata's bayes prefix to fit Bayesian models.