Template-Type: ReDIF-Article 1.0 Author-Name: Matteo Bottai Author-Workplace-Name: Karolinska Institutet Author-Email: matteo.bottai@ki.se Author-Name: Nicola Orsini Author-Workplace-Name: Karolinska Institutet Author-Email: nicola.orsini@ki.se Title: qmodel: A command for fitting parametric quantile models Journal: Stata Journal Pages: 261-293 Issue: 2 Volume: 19 Year: 2019 Month: June X-DOI: 10.1177/1536867X19854002 Abstract: In this article, we introduce the qmodel command, which fits para- metric models for the conditional quantile function of an outcome variable given covariates. Ordinary quantile regression, implemented in the qreg command, is a popular, simple type of parametric quantile model. It is widely used but known to yield erratic estimates that often lead to uncertain inferences. Parametric quantile models overcome these limitations and extend modeling of conditional quantile functions beyond ordinary quantile regression. These models are flexible and ef- ficient. qmodel can estimate virtually any possible linear or nonlinear parametric model because it allows the user to specify any combination of qmodel-specific built-in functions, standard mathematical and statistical functions, and substi- tutable expressions. We illustrate the potential of parametric quantile models and the use of the qmodel command and its postestimation commands through real- and simulated-data examples that commonly arise in epidemiological and pharmacological research. In addition, this article may give insight into the close connection that exists between quantile functions and the true mathematical laws that generate data. Copyright 2019 by StataCorp LP. Keywords: qmodel, qmodel postestimation, predict, qmodel quantile, qmodel plot, quantile regression, quantile regression coefficient models, integrated loss function File-URL: http://www.stata-journal.com/article.html?article=st0555 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj19-2/st0555/ File-Format: text/html Handle:RePEc:tsj:stataj:v:19:y:2019:i:2:p:261-293 Template-Type: ReDIF-Article 1.0 Author-Name: Legrand D. F. Saint-Cyr Author-Workplace-Name: SMART–LERECO, Agrocamus-Ouest, INRA Author-Email: legrand.saint-cyr@inra.fr Author-Name: Laurent Piet Author-Workplace-Name: SMART–LERECO, Agrocamus-Ouest, INRA Author-Email: laurent.piet@inra.fr Title: mixmcm: A community-contributed command for fitting mixtures of Markov chain models using maximum likelihood and the EM algorithm Journal: Stata Journal Pages: 294-334 Issue: 2 Volume: 19 Year: 2019 Month: June X-DOI: 10.1177/1536867X19854015 Abstract: Markov chain models and finite mixture models have been widely applied in various strands of the academic literature. Several studies analyzing dynamic processes have combined both modeling approaches to account for un- observed heterogeneity within a population. In this article, we describe mixmcm, a community-contributed command that fits the general class of mixed Markov chain models, accounting for the possibility of both entries into and exits from the population. To account for the possibility of incomplete information within the data (that is, unobserved heterogeneity), the model is fit with maximum like- lihood using the expectation-maximization algorithm. mixmcm enables users to fit the mixed Markov chain models parametrically or semiparametrically, depending on the specifications chosen for the transition probabilities and the mixing distri- bution. mixmcm also allows for endogenous identification of the optimal number of homogeneous chains, that is, unobserved types or “components”. We illustrate mixmcm’s usefulness through three examples analyzing farm dynamics using an unbalanced panel of commercial French farms. Copyright 2019 by StataCorp LP. Keywords: mixmcm, Markov chain model, finite mixture model, EM algorithm, mlogit, fmlogit File-URL: http://www.stata-journal.com/article.html?article=st0556 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj19-2/st0556/ File-Format: text/html Handle:RePEc:tsj:stataj:v:19:y:2019:i:2:p:294-334 Template-Type: ReDIF-Article 1.0 Author-Name: Yutao Sun Author-Workplace-Name: Northeast Normal University Author-Email: sunyt100@nenu.edu.cn Author-Name: Geert Dhaene Author-Workplace-Name: KU Leuven Author-Email: geert.dhaene@kuleuven.be Title: xtspj: A command for split-panel jackknife estimation Journal: Stata Journal Pages: 335-374 Issue: 2 Volume: 19 Year: 2019 Month: June X-DOI: 10.1177/1536867X19854016 Abstract: In this article, we present a new command, xtspj, that corrects for incidental parameter bias in panel-data models with fixed effects. The correc- tion removes the first-order bias term of the maximum likelihood estimate using the split-panel jackknife method. Two variants are implemented: the jackknifed maximum-likelihood estimate and the jackknifed log-likelihood function (with cor- responding maximizer). The model may be nonlinear or dynamic, and the covari- ates may be predetermined instead of strictly exogenous. xtspj implements the split-panel jackknife for fixed-effects versions of linear, probit, logit, Poisson, ex- ponential, gamma, Weibull, and negbin2 regressions. It also accommodates other models if the user specifies the log-likelihood function (and, possibly but not nec- essarily, the score function and the Hessian). xtspj is fast and memory efficient, and it allows large datasets. The data may be unbalanced. xtspj can also be used to compute uncorrected maximum-likelihood estimates of fixed-effects models for which no other xt (see [XT] xt) command exists. Copyright 2019 by StataCorp LP. Keywords: xtspj, split-panel jackknife, incidental parameter problem, maximum likelihood File-URL: http://www.stata-journal.com/article.html?article=st0557 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj19-2/st0557/ File-Format: text/html Handle:RePEc:tsj:stataj:v:19:y:2019:i:2:p:335-374 Template-Type: ReDIF-Article 1.0 Author-Name: Jesper N. Wulff Author-Workplace-Name: Aarhus University Author-Email: jwulff@econ.au.dk Title: Generalized two-part fractional regression with cmp Journal: Stata Journal Pages: 375-389 Issue: 2 Volume: 19 Year: 2019 Month: June X-DOI: 10.1177/1536867X19854017 Abstract: Researchers who model fractional dependent variables often need to consider whether their data were generated by a two-part process. Two-part mod- els are ideal for modeling two-part processes because they allow us to model the participation and magnitude decisions separately. While community-contributed commands currently facilitate estimation of two-part models, no specialized com- mand exists for fitting two-part models with process dependency. In this article, I describe generalized two-part fractional regression, which allows for dependency between models’ parts. I show how this model can be fit using the community- contributed cmp command (Roodman, 2011, Stata Journal 11: 159–206). I use a data example on the financial leverage of firms to illustrate how cmp can be used to fit generalized two-part fractional regression. Furthermore, I show how to obtain predicted values of the fractional dependent variable and marginal effects that are useful for model interpretation. Finally, I show how to compute model fit statistics and perform the RESET test, which are useful for model evaluation. Copyright 2019 by StataCorp LP. Keywords: generalized two-part fractional regression, process dependence, fractional probit, cmp File-URL: http://www.stata-journal.com/article.html?article=st0558 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj19-2/st0558/ File-Format: text/html Handle:RePEc:tsj:stataj:v:19:y:2019:i:2:p:375-389 Template-Type: ReDIF-Article 1.0 Author-Name: Chi-lin Tsai Author-Workplace-Name: National Chengchi University Author-Email: chi.lin.tsai.clt@gmail.com Title: Statistical analysis of the item-count technique using Stata Journal: Stata Journal Pages: 390-434 Issue: 2 Volume: 19 Year: 2019 Month: June X-DOI: 10.1177/1536867X19854018 Abstract: In this article, I review recent developments of the item-count technique (also known as the unmatched-count or list-experiment technique) and introduce a new package, kict, for statistical analysis of the item-count data. This package contains four commands: kict deff performs a diagnostic test to detect the vio- lation of an assumption underlying the item-count technique. kict ls and kict ml perform least-squares estimation and maximum likelihood estimation, respec- tively. Each encompasses a number of estimators, offering great flexibility for data analysis. kict pfci is a postestimation command for producing confidence in- tervals with better coverage based on profile likelihood. The development of the item-count technique is still ongoing. I will continue to update the kict package accordingly. Copyright 2019 by StataCorp LP. Keywords: kict, kict deff, kict ls, kict ml, kict pfci, item-count technique, unmatched-count technique, list experiment, sensitive question File-URL: http://www.stata-journal.com/article.html?article=st0559 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj19-2/st0559/ File-Format: text/html Handle:RePEc:tsj:stataj:v:19:y:2019:i:2:p:390-434 Template-Type: ReDIF-Article 1.0 Author-Name: Clément de Chaisemartin Author-Workplace-Name: University of California at Santa Barbara Author-Email: clementdechaisemartin@ucsb.edu Author-Name: Xavier D’Haultfoeuille Author-Workplace-Name: CREST Author-Email: xavier.dhaultfoeuille@ensae.fr Author-Name: Yannick Guyonvarch Author-Workplace-Name: CREST Author-Email: yannick.guyonvarch@ensae.fr Title: Fuzzy differences-in-differences with Stata Journal: Stata Journal Pages: 435-458 Issue: 2 Volume: 19 Year: 2019 Month: June X-DOI: 10.1177/1536867X19854019 Abstract: Differences-in-differences evaluates the effect of a treatment. In its basic version, a “control group” is untreated at two dates, whereas a “treatment group” becomes fully treated at the second date. However, in many applications of this method, the treatment rate increases more only in the treatment group. In such fuzzy designs, de Chaisemartin and D’Haultfœuille (2018b, Review of Eco- nomic Studies 85: 999–1028) propose various estimands that identify local average and quantile treatment effects under different assumptions. They also propose estimands that can be used in applications with a nonbinary treatment, multi- ple periods, and groups and covariates. In this article, we present the command fuzzydid, which computes the various corresponding estimators. We illustrate the use of the command by revisiting Gentzkow, Shapiro, and Sinkinson (2011, American Economic Review 101: 2980–3018). Copyright 2019 by StataCorp LP. Keywords: fuzzydid, differences-in-differences, fuzzy designs, local average treatment effects, local quantile treatment effects File-URL: http://www.stata-journal.com/article.html?article=st0560 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj19-2/st0560/ File-Format: text/html Handle:RePEc:tsj:stataj:v:19:y:2019:i:2:p:435-458 Template-Type: ReDIF-Article 1.0 Author-Name: John Luke Gallup Author-Workplace-Name: Portland State University Author-Email: jlgallup@pdx.edu Title: Grade functions Journal: Stata Journal Pages: 459-476 Issue: 2 Volume: 19 Year: 2019 Month: June X-DOI: 10.1177/1536867X19854020 Abstract: Student grade processing using Stata is more reliable than methods like spreadsheets and saves the user time, especially when courses are repeated. In this article, I introduce functions that automate some useful grade calculations: the functions curve grades according to combinations of a target grade mean, maximum, standard deviation, and percentile cutoff; convert between numerical grades and letter grades; and convert between 0–100 grades and 0–4 grades (grade point average). The functions can also convert between other grading scales, such as those used in other countries. Keywords: grade curve(), grade pct(), gradetoAF(), gradeofAF(), gradeto04(), gradeof04(), egen functions, curving grades, letter grades, z scores, quantiles, grade conversion File-URL: http://www.stata-journal.com/article.html?article=st0561 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj19-2/st0561/ File-Format: text/html Handle:RePEc:tsj:stataj:v:19:y:2019:i:2:p:459-476 Template-Type: ReDIF-Article 1.0 Author-Name: Jacopo Lenzi Author-WorkPlace-Name: Alma Mater Studiorum–University of Bologna Author-Email: jacopo.lenzi2@unibo.it Author-Name: Santa Pildava Author-WorkPlace-Name: Centre for Disease Prevention and Control of Latvia Author-Email: santa.pildava@spkc.gov.lv Title: Tips for calculating and displaying risk-standardized hospital outcomes in Stata Journal: Stata Journal Pages: 477-496 Issue: 2 Volume: 19 Year: 2019 Month: June X-DOI: 10.1177/1536867X19854021 Abstract: A major challenge of outcomes research is measuring hospital perfor-mance using readily available administrative data. When the outcome measure is mortality or morbidity, rates are adjusted to account for preexisting conditions that may confound their assessment. However, the concept of “risk-adjusted” outcomes is frequently misunderstood. In this article, we try to clarify things, and we describe Stata tools for appropriately calculating and displaying risk-standardized outcome measures. We offer practical guidance and illustrate the application of these tools to an example based on real data (30-day mortality following acute myocardial infarction in Latvia). Keywords: risk adjustment, bootstrap, caterpillar plot, eclplot, funnel plot, generalized estimating equations, healthcare quality assessment, hospital profil- ing, mfpboot, mfpboot bif, multivariable modeling, outcomes research, qic, risk-standardized mortality rates, stability, xtgee File-URL: http://www.stata-journal.com/article.html?article=st0562 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj19-2/st0562/ File-Format: text/html Handle:RePEc:tsj:stataj:v:19:y:2019:i:2:p:477-496 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 497 Issue: 2 Volume: 19 Year: 2019 Month: June Abstract: Updates for previously published packages are provided. File-URL: http://www.stata-journal.com/software/sj19-2/st0143_5/ File-Format: text/html File-URL: http://www.stata-journal.com/software/sj19-2/st0375_2/ File-Format: text/html File-URL: http://www.stata-journal.com/software/sj19-2/st0532_1/ File-Format: text/html Note: Windows users should not attempt to download these files with a web browser. Handle:RePEc:tsj:stataj:v:19:y:2019:i:2:p:497