bluuebunny/crossref_metadata_2025 huggingface dataset:
Gpsbound: A Command for Importing and Verifying Geographical Information from a User-provided Shapefile
Authors: Brophy Tim S. L. , Daniels Reza Che , Musundwa Sibongile
Published: November 2018

Geographical coordinates such as Global Positioning System (GPS) latitude and longitude estimates form the foundation of many spatial statistical methods. gpsbound allows users to 1) import geographical information from the attribute table of a polygon shapefile based on the identified location of GPS coordinates in a Stata dataset, and 2) check whether the GPS coordinates lie within the bounds of a polygon demarcated in the shapefile (for example, enumeration areas or primary sampling units). gpsbound also allows users to work with spatial data in Stata without the use of Geographical Information System software.
Estimating Almost-ideal Demand Systems with Endogenous Regressors
Authors: Lecocq Sébastien , Robin Jean-Marc
Published: November 2018

In this article, we present the new aidsills command for estimating almost-ideal demand systems and their quadratic extensions. In contrast with Poi's (2012, Stata Journal 12: 433–446) quaids command, which is based on the nonlinear nlsur command, aidsills uses the computationally attractive iterated linear least-squares estimator developed by Blundell and Robin (1999, Journal of Applied Econometrics 14: 209–232). The new command further allows one to account for endogenous prices and total expenditure by using instrumental-variable techniques. Elasticities and their standard errors can be obtained using the aidsills elas postestimation command.
Review of Alan Acock's a Gentle Introduction to Stata, Fourth Edition
Authors: Collier Tim
Published: November 2018

In this article, I review A Gentle Introduction to Stata, Fourth Edition, by Alan Acock (2014 [Stata Press]).
Precombine: A Command to Examine n ≥ 2 Datasets before Combining
Authors: Chatfield Mark D.
Published: November 2018

In this article, I present a new command, precombine, that alerts the user to, and provides assurance concerning, some problems that can occur when multiple Stata datasets are merged and appended. It describes variables that are common to multiple datasets as well as variables that are unique to one dataset. Where value labels are attached to variables, it checks whether code sets are identical across datasets. Summary statistics for values of all variables can also be listed and left in memory along with the descriptions of each variable of each dataset.
Fitting Fixed- and Random-effects Meta-analysis Models Using Structural Equation Modeling with the Sem and Gsem Commands
Authors: Palmer Tom M. , Sterne Jonathan A. C.
Published: November 2018

In this article, we demonstrate how to fit fixed- and random-effects meta-analysis, meta-regression, and multivariate outcome meta-analysis models under the structural equation modeling framework using the sem and gsem commands. While all of these models can be fit using existing user-written commands, formulating the models in the structural equation modeling framework provides deeper insight into how they work. Further, the heterogeneity test for meta-regression and multivariate meta-analysis is readily available from this output.
Estimation of Mean Health Care Costs and Incremental Cost-effectiveness Ratios with Possibly Censored Data
Authors: Chen Shuai , Rolfes Jennifer , Zhao Hongwei
Published: November 2018

In this article, we describe the hcost program for estimating mean health care costs and incremental cost-effectiveness ratios with possibly censored data. hcost estimates the mean survival time and the mean costs, as well as their variances and covariance, for a given time horizon. If the group variable is specified, hcost will report the differences between two groups, as well as the incremental cost-effectiveness ratio and its confidence interval (optional). hcost can estimate the mean costs using two methods corresponding to different types of data: the Bang and Tsiatis (2000, Biometrika 87: 329–343) estimator using only the total costs or the Zhao and Tian (2001, Biometrics 57: 1002–1008) estimator when cost-history data are available. The hcost program can also be used to specify the annual discounting rates for survival time and costs.
Approximate Bayesian Logistic Regression via Penalized Likelihood by Data Augmentation
Authors: Discacciati Andrea , Orsini Nicola , Greenland Sander
Published: November 2018

We present a command, penlogit, for approximate Bayesian logistic regression using penalized likelihood estimation via data augmentation. This command automatically adds specific prior-data records to a dataset. These records are computed so that they generate a penalty function for the log likelihood of a logistic model, which equals (up to an additive constant) a set of independent log prior distributions on the model parameters. This command overcomes the necessity of relying on specialized software and statistical tools (such as Markov chain Monte Carlo) for fitting Bayesian models, and allows one to assess the information content of a prior in terms of the data that would be required to generate the prior as a likelihood function. The command produces data equivalent to normal and generalized log- F priors for the model parameters, providing flexible translation of background information into prior data, which allows calculation of approximate posterior medians and intervals from ordinary maximum likelihood programs. We illustrate the command through an example using data from an observational study of neonatal mortality.
Fitting Adjusted Limited Dependent Variable Mixture Models to EQ-5D
Authors: Alava Mónica Hernández , Wailoo Allan
Published: November 2018

In this article, we describe the aldvmm command for fitting adjusted limited dependent variable mixture models to either UK or U.S. tariff EQ-5D data. We present and explain the command and postestimation command through examples. The aldvmm command requires use of Stas Kolenikov's simulated annealing package (simann()), which can be easily installed by typing net install simann.pkg, from( http://web.missouri.edu/∼kolenikovs/stata ).
Creating Summary Tables Using the Sumtable Command
Authors: Scott Lauren J. , Rogers Chris A.
Published: November 2018

In many fields of statistics, summary tables are used to describe characteristics within a study population and are often used to compare characteristics of two or more groups. The sumtable command can be used to quickly and easily produce such summary tables, allowing for different types of data to be summarized within one table. In this article, we provide examples and advice for its use.
Tests for Normality in Linear Panel-data Models
Authors: Alejo Javier , Galvao Antonio , Montes-Rojas Gabriel , Sosa-Escudero Walter
Published: November 2018

We propose a new command, xtsktest, for explaining nonnormalities in linear panel-data models. The command performs tests to explore skewness and excess kurtosis, allowing researchers to identify departures from Gaussianity in both error components of a standard panel regression, separately or jointly. The tests are based on recent results by Galvao et al. (2013, Journal of Multivariate Analysis 122: 35–52) and extend the classical Jarque–Bera normality test for the case of panel data.
Model Specification and Bootstrapping for Multiply Imputed Data: An Application to Count Models for the Frequency of Alcohol Use
Authors: Comulada W. Scott
Published: November 2018

Stata's mi commands provide powerful tools to conduct multiple imputation in the presence of ignorable missing data. In this article, I present Stata code to extend the capabilities of the mi commands to address two areas of statistical inference where results are not easily aggregated across imputed datasets. First, mi commands are restricted to covariate selection. I show how to address model fit to correctly specify a model. Second, the mi commands readily aggregate model-based standard errors. I show how standard errors can be bootstrapped for situations where model assumptions may not be met. I illustrate model specification and bootstrapping on frequency counts for the number of times that alcohol was consumed in data with missing observations from a behavioral intervention.
EORTC QLQ-C30 Descriptive Analysis with the qlqc30 Command
Authors: Bascoul-Mollevi Caroline , Castan Florence , Azria David , Gourgou-Bourgade Sophie
Published: November 2018

Health-related quality of life is often an endpoint in oncology clinical trials. The European Organization for Research and Treatment of Cancer (EORTC) developed the cancer-specific quality of life questionnaire (QLQ-C30), which includes five functions, nine symptoms, and a global health status. These questionnaires are completed by the patients themselves throughout the process of care. The recommended approaches for processing EORTC QLQ-C30 data are usually descriptive and graphic.

Our aim was to develop a user-written command that provided an automatic descriptive analysis of EORTC QLQ-C30 data, consisting of profile plots per visit and longitudinal plots per functional and symptom scale.
Implementing a Strategy to Reduce the Instrument Count in Panel GMM
Authors: Bontempi Maria Elena , Mammi Irene
Published: November 2018

The problem of instrument proliferation and its consequences—overfitting of the endogenous explanatory variables, biased instrumental-variables and generalized method of moments estimators, and weakening of the power of the overidentification tests—are well known. This article introduces a statistical method to reduce the instrument count. Principal component analysis is applied on the instrument matrix, and the principal-component analysis scores are used as instruments for the panel generalized method of moments estimation. This strategy is implemented through the new command pca2.
Estimating the Treatment Effect in a Clinical Trial Using Difference in Restricted Mean Survival Time
Authors: Royston Patrick
Published: November 2018

The causal effect of a new medical treatment compared with a standard regimen is best assessed in a randomized controlled trial setting. When the main outcome is time to some event of interest, such as death, studies often use the hazard ratio to describe the treatment effect. Typically, proportional hazards are assumed. Here I discuss several significant disadvantages of using the hazard ratio, including its vulnerability to the proportionality assumption, its relative nature, and its lack of relationship with time-to-event or survival probabilities. I describe the use of restricted mean survival time as an alternative outcome measure in time-to-event trials. With this method, the treatment effect is defined as the difference in restricted mean between the trial arms. I suggest the use of Royston and Parmar's (2002, Statistics in Medicine 21: 2175–2197) class of flexible parametric models, implemented through the command stpm2 (Lambert and Royston [2009, Stata Journal 9: 265–290] and Andersson and Lambert [2012, Stata Journal 12: 623–638]), to estimate the required quantities. With this approach, proportional hazards are not assumed. I describe a new command, strmst, for implementing these calculations. This method supports “direct” adjustment for covariates by using marginalization over their observed distribution, and it supports estimation of treatment effects conditional on fixed values of covariates. I illustrate the methodology using data from a trial in primary biliary cirrhosis. I provide an example that demonstrates the importance of understanding the relationship between the treatment effect, the prognosis of the disease outcome, and the often-neglected time domain.
Regressions are Commonly Misinterpreted
Authors: Hoaglin David C.
Published: November 2018

Much literature misinterprets results of fitting multivariable models for linear regression, logistic regression, and other generalized linear models, as well as for survival, longitudinal, and hierarchical regressions. For the leading case of multiple regression, regression coefficients can be accurately interpreted via the added-variable plot. However, a common interpretation does not reflect the way regression methods actually work. Additional support for the correct interpretation comes from examining regression coefficients in multivariate normal distributions and from the geometry of least squares. To properly implement multivariable models, one must be cautious when calculating predictions that average over other variables, as in the Stata command margins.
Bireprob: An Estimator for Bivariate Random-effects Probit Models
Authors: Plum Alexander
Published: November 2018

I present the bireprob command, which fits a bivariate random-effects probit model. bireprob enables a researcher to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between their unobservables. The estimator uses quasirandom numbers (Halton draws) and maximum simulated likelihood to estimate the correlation between the error terms of both processes. The application of bireprob is illustrated in two examples: the first one uses artificial data, and the second one uses real data. Finally, in a simulation, the performance of the estimator is tested and compared with the official Stata command xtprobit.
Bicop: A Command for Fitting Bivariate Ordinal Regressions with Residual Dependence Characterized by a Copula Function and Normal Mixture Marginals
Authors: Hernández-Alava Mónica , Pudney Stephen
Published: November 2018

In this article, we describe a new Stata command, bicop, for fitting a model consisting of a pair of ordinal regressions with a flexible residual distribution, with each marginal distribution specified as a two-part normal mixture, and stochastic dependence governed by a choice of copula functions. The bicop command generalizes the existing biprobit and bioprobit commands, which assume a bivariate normal residual distribution. We present and explain the bicop estimation command and the available postestimation commands using data on financial well-being from the UK Understanding Society Panel Survey.
Implementing Factor Models for Unobserved Heterogeneity in Stata
Authors: Sarzosa Miguel , Urzúa Sergio
Published: November 2018

We introduce a new command, heterofactor, for the maximum likelihood estimation of models with unobserved heterogeneity, including a Roy model. heterofactor fits models with up to four latent factors and allows the unobserved heterogeneity to follow general distributions. Our command differs from Stata's sem command in that it does not rely on the linearity of the structural equations and distributional assumptions for identification of the unobserved heterogeneity. It uses the estimated distributions to numerically integrate over the unobserved factors in the outcome equations by using a mixture of normals in a Gauss–Hermite quadrature. heterofactor delivers consistent estimates, including the unobserved factor loadings, in a variety of model structures.
Fixed Effects in Unconditional Quantile Regression
Authors: Borgen Nicolai T.
Published: November 2018

Unconditional quantile regression has quickly become popular after being introduced by Firpo, Fortin, and Lemieux (2009, Econometrica 77: 953–973) and is easily implemented using the user-written command rifreg by the same authors. However, including high-dimensional fixed effects in rifreg is quite burdensome and sometimes even impossible. In this article, I show that when the number of fixed effects is large, the computational speed is massively increased by using xtreg rather than regress to fit the unconditional quantile regression models. I also introduce the xtrifreg command, which should be considered a supplement to rifreg. The xtrifreg command has many of the same features as rifreg but can be used to include a large number of fixed effects, to estimate cluster–robust standard errors, and to estimate cluster–bootstrapped standard errors.
Panel Time Series: Review of the Methodological Evolution
Authors: Burdisso Tamara , Sangiácomo Máximo
Published: November 2018

In this article, we discuss the econometric treatment of macropanels, also known as panel time series. This new approach rejects the assumption of slope homogeneity and handles nonstationarity. It also recognizes that cross-section dependence (that is, some correlation structure in the error term between units due to unobservable common factors) squanders efficiency gains by operating with a panel. This approach uses a new set of estimators known in the literature as the common correlated effect, which essentially consists of increasing the model to be fit by adding the averages of the individuals in each time t, of both the dependent variable and the specific regressors of each individual. We present two commands developed for the evaluation and treatment of cross-section dependence.
Reference-based Sensitivity Analysis via Multiple Imputation for Longitudinal Trials with Protocol Deviation
Authors: Cro Suzie , Morris Tim P. , Kenward Michael G. , Carpenter James R.
Published: November 2018

Randomized controlled trials provide essential evidence for the evaluation of new and existing medical treatments. Unfortunately, the statistical analysis is often complicated by the occurrence of protocol deviations, which mean we cannot always measure the intended outcomes for individuals who deviate, resulting in a missing-data problem. In such settings, however one approaches the analysis, an untestable assumption about the distribution of the unobserved data must be made. To understand how far the results depend on these assumptions, the primary analysis should be supplemented by a range of sensitivity analyses, which explore how the conclusions vary over a range of different credible assumptions for the missing data. In this article, we describe a new command, mimix, that can be used to perform reference-based sensitivity analyses for randomized controlled trials with longitudinal quantitative outcome data, using the approach proposed by Carpenter, Roger, and Kenward (2013, Journal of Biopharmaceutical Statistics 23: 1352–1371). Under this approach, we make qualitative assumptions about how individuals’ missing outcomes relate to those observed in relevant groups in the trial, based on plausible clinical scenarios. Statistical analysis then proceeds using the method of multiple imputation.
Nonparametric Frontier Analysis Using Stata
Authors: Badunenko Oleg , Mozharovskyi Pavlo
Published: November 2018

In this article, we describe five new Stata commands that fit and provide statistical inference in nonparametric frontier models. The tenonradial and teradial commands fit data envelopment models where nonradial and radial technical efficiency measures are computed (Färe, 1998, Fundamentals of Production Theory; Färe and Lovell, 1978, Journal of Economic Theory 19: 150–162; Färe, Grosskopf, and Lovell, 1994a, Production Frontiers). Technical efficiency measures are obtained by solving linear programming problems. The teradialbc, nptestind, and nptestrts commands provide tools for making statistical inference regarding radial technical efficiency measures (Simar and Wilson, 1998, Management Science 44: 49–61; 2000, Journal of Applied Statistics 27: 779–802; 2002, European Journal of Operational Research 139: 115–132). We provide a brief overview of the nonparametric efficiency measurement, and we describe the syntax and options of the new commands. Additionally, we provide an example showing the capabilities of the new commands. Finally, we perform a small empirical study of productivity growth.
A Sparser, Speedier Reshape
Authors: Simons Kenneth L.
Published: November 2018

A new command, sreshape, supports sparse and speedy reshaping of data. Often reshaped data are “sparse” in the sense of containing many missing values that are dropped after reshaping. sreshape automates the process of reshaping and dropping such missing information to avoid potential errors and, for both sparse and nonsparse data, yields speed improvements. Using large test datasets, sreshape achieves identical results to reshape 8 to 31 times faster in wide-to-long reshapes and 2 to 13 times faster in long-to-wide reshapes. Further suggested improvements may allow StataCorp to increase these speed gains in the built-in version.
Using mi Impute Chained to fit ANCOVA Models in Randomized Trials with Censored Dependent and Independent Variables
Authors: Andersen Andreas , Rieckmann Andreas
Published: November 2018

In this article, we illustrate how to use mi impute chained with intreg to fit an analysis of covariance analysis of censored and nondetectable immunological concentrations measured in a randomized pretest–posttest design.
Estimation of Panel Vector Autoregression in Stata
Authors: Abrigo Michael R. M. , Love Inessa
Published: November 2018

Panel vector autoregression (VAR) models have been increasingly used in applied research. While programs specifically designed to fit time-series VAR models are often included as standard features in most statistical packages, panel VAR model estimation and inference are often implemented with general-use routines that require some programming dexterity. In this article, we briefly discuss model selection, estimation, and inference of homogeneous panel VAR models in a generalized method of moments framework, and we present a set of programs to conveniently execute them. We illustrate the pvar package of programs by using standard Stata datasets.
Distributional Estimates for the Comparison of Proportions of a Dichotomized Continuous Outcome
Authors: Sauzet Odile , Kleine Maren
Published: November 2018

We present the package distdicho, which contains a range of commands covering the present development of the distributional method for the dichotomization of continuous outcomes. The method provides estimates with standard error of a comparison of proportions (difference, odds ratio, and risk ratio) derived, with similar precision, from a comparison of means.
Production Function Estimation in Stata Using the Ackerberg–Caves–Frazer Method
Authors: Manjón Miguel , Mañez Juan
Published: November 2018

We present a new e-class command, acfest, that implements the method of Ackerberg, Caves, and Frazer (2015, Econometrica 83: 2411–2451) to estimate production functions. This method deals with the functional dependence problems that may arise in the methods proposed by Olley and Pakes (1996, Econometrica 64: 1263–1297) and, particularly, Levinsohn and Petrin (2003, Review of Economic Studies 70: 317–341) (implemented in Stata by Yasar, Raciborski, and Poi [2008, Stata Journal 8: 221–231] and Petrin, Poi, and Levinsohn [2004, Stata Journal 4: 113–123], respectively). In particular, the acfest command yields (nonlinear, robust) generalized method of moments estimates using a Mata function and two specification tests (Wald and Sargan–Hansen). After estimation, predict provides the estimated productivity of the firms in the sample.
Speaking Stata: Letter Values as Selected Quantiles
Authors: Cox Nicholas J.
Published: November 2018

Letter values were introduced and named by J. W. Tukey in the 1970s as selected quantiles. The idea is to choose a small set of quantiles to characterize an ordered sample of values, quantiles that are each defined as either individual order statistics or the mean of two such statistics. The procedure is to start with the median, to continue with quartiles, and then with first and last octiles, and so on. At each step, approximate medians are identified of successively smaller tail fractions until the extremes, the minimum and maximum, are reached. As a historical aside, the same idea can be identified in work by Francis Galton from 1880.

Letter values are supported by Stata through the official command lv, but that command is geared to letter-value displays and (arbitrarily) will compute no more than 21 letter values. A new command, lvalues, is introduced that supports calculation of letter values without such limits and is designed to save results in new variables for as many variables and distinct groups as are specified. Results may then be easily listed and (especially) plotted in pursuit of identification and comparison of distribution level, spread, and shape. Examples are given with emphasis on quantile plots.
Fitting Endogenous Stochastic Frontier Models in Stata
Authors: Karakaplan Mustafa U.
Published: November 2018

In this article, I introduce sfkk, a new command for fitting endogenous stochastic frontier models. sfkk provides estimators for the parameters of a linear model with a disturbance assumed to be a mixture of two components: a measure of inefficiency that is strictly nonnegative and a two-sided error term from a symmetric distribution. sfkk can handle endogenous variables in the frontier or the inefficiency, and the sfkk estimates outperform the standard frontier estimates that ignore endogeneity.
Within- and Between-cluster Effects in Generalized Linear Mixed Models: A Discussion of Approaches and the Xthybrid command
Authors: Schunck Reinhard , Perales Francisco
Published: November 2018

One typically analyzes clustered data using random- or fixed-effects models. Fixed-effects models allow consistent estimation of the effects of level-one variables, even if there is unobserved heterogeneity at level two. However, these models cannot estimate the effects of level-two variables. Hybrid and correlated random-effects models are flexible modeling specifications that separate within-and between-cluster effects and allow for both consistent estimation of level-one effects and inclusion of level-two variables. In this article, we elaborate on the separation of within- and between-cluster effects in generalized linear mixed models. These models present a unifying framework for an entire class of models whose response variables follow a distribution from the exponential family (for example, linear, logit, probit, ordered probit and logit, Poisson, and negative binomial models). We introduce the user-written command xthybrid, a shell for the meglm command. xthybrid can fit a variety of hybrid and correlated random-effects models.
Heuristic Criteria for Selecting an Optimal Aspect Ratio in a Two-variable Line Plot
Authors: Christodoulou Demetris
Published: November 2018

Line plots encode a series of slopes from adjoining coordinates and aim to reveal suggestive patterns in the sequential rates of change. The choice of aspect ratio imposed on the line plot largely determines the judged prevalence of patterns in the bivariate series and the degree of steepness in the rates of change. Choosing an appropriate aspect ratio is key to designing informative line plots. The optaspect command calculates the optimal aspect ratio in a two-variable line graph using a number of heuristic criteria.
Regression Clustering for Panel-data Models with Fixed Effects
Authors: Christodoulou Demetris , Sarafidis Vasilis
Published: November 2018

In this article, we describe the xtregcluster command, which implements the panel regression clustering approach developed by Sarafidis and Weber (2015, Oxford Bulletin of Economics and Statistics 77: 274–296). The method classifies individuals into clusters, so that within each cluster, the slope parameters are homogeneous and all intracluster heterogeneity is due to the standard two-way error-components structure. Because the clusters are heterogeneous, they do not share common parameters. The number of clusters and the optimal partition are determined by the clustering solution, which minimizes the total residual sum of squares of the model subject to a penalty function that strictly increases in the number of clusters. The method is available for linear short panel-data models and useful for exploring heterogeneity in the slope parameters when there is no a priori knowledge about parameter structures. It is also useful for empirically evaluating whether any normative classifications are justifiable from a statistical point of view.
A Flexible Parametric Competing-risks Model Using a Direct Likelihood Approach for the Cause-specific Cumulative Incidence Function
Authors: Mozumder Sarwar Islam , Rutherford Mark J. , Lambert Paul C.
Published: November 2018

In competing-risks analysis, the cause-specific cumulative incidence function (CIF) is usually obtained in a modeling framework by either 1) transforming on all cause-specific hazards or 2) transforming by using a direct relationship with the subdistribution hazard function. We expand on current competing-risks methodology from within the flexible parametric survival modeling framework and focus on the second approach. This approach models all cause-specific CIFs simultaneously and is more useful for answering prognostic-related questions. We propose the direct flexible parametric survival modeling approach for the cause-specific CIF. This approach models the (log cumulative) baseline hazard without requiring numerical integration, which leads to benefits in computational time. It is also easy to make out-of-sample predictions to estimate more useful measures and incorporate alternative link functions, for example, logit links. To implement these methods, we introduce a new estimation command, stpm2cr, and demonstrate useful predictions from the model through an illustrative melanoma dataset.
Rate Decomposition for Aggregate Data Using Das Gupta's Method
Authors: Li Jinjing
Published: November 2018

Social, behavioral, and health scientists frequently decompose changes or differences in outcome variables into components of change and assess their relative importance. Many Stata commands facilitate this exercise using unit-level data, notably by applying the Blinder–Oaxaca approach. However, none of the comparable user-written commands decompose changes or differences in aggregate data despite their availability and the widespread use of corresponding decomposition techniques. In this article, I present the user-written command rdecompose, which decomposes aggregate or cross-classified data based on Das Gupta's (1993, Standardization and Decomposition of Rates: A User's Manual, Volume 1) approach, and demonstrate its application in multiple settings. This command extends the original method by allowing multiple factors and flexible functional specifications.
Bias Corrections for Probit and Logit Models with Two-way Fixed Effects
Authors: Cruz-Gonzalez Mario , Fernández-Val Iván , Weidner Martin
Published: November 2018

In this article, we present the user-written commands probitfe and logitfe, which fit probit and logit panel-data models with individual and time unobserved effects. Fixed-effects panel-data methods that estimate the unobserved effects can be severely biased because of the incidental parameter problem (Neyman and Scott, 1948, Econometrica 16: 1–32). We tackle this problem using the analytical and jackknife bias corrections derived in Fernández-Val and Weidner (2016, Journal of Econometrics 192: 291–312) for panels where the two dimensions ( N and T) are moderately large. We illustrate the commands with an empirical application to international trade and a Monte Carlo simulation calibrated to this application.
SADI: Sequence Analysis Tools for Stata
Authors: Halpin Brendan
Published: November 2018

The SADI package provides tools for sequence analysis, which focuses on the similarity and dissimilarity between categorical time series such as life-course trajectories. SADI‘s main components are tools to calculate intersequence distances using several different algorithms, including the optimal matching algorithm, but it also includes utilities to graph, summarize, and manage sequence data. It provides similar functionality to the R package TraMineR and the Stata package SQ but is substantially faster than the latter.
How to Test for Goodness of Fit in Ordinal Logistic Regression Models
Authors: Fagerland Morten W. , Hosmer David W.
Published: November 2018

Ordinal regression models are used to describe the relationship between an ordered categorical response variable and one or more explanatory variables. Several ordinal logistic models are available in Stata, such as the proportional odds, adjacent-category, and constrained continuation-ratio models. In this article, we present a command (ologitgof) that calculates four goodness-of-fit tests for assessing the overall adequacy of these models. These tests include an ordinal version of the Hosmer–Lemeshow test, the Pulkstenis–Robinson chi-squared and deviance tests, and the Lipsitz likelihood-ratio test. Together, these tests can detect several different types of lack of fit, including wrongly specified continuous terms, omission of different types of interaction terms, and an unordered response variable.
Response Surface Models for OLS and GLS Detrending-based Unit-root Tests in Nonlinear ESTAR Models
Authors: Otero Jesús , Smith Jeremy
Published: November 2018

In this article, we calculate response surface models for a large range of quantiles of the Kapetanios, Shin, and Snell (2003, Journal of Econometrics 112: 359–379) and Kapetanios and Shin (2008, Economics Letters 100: 377–380) tests for the null hypothesis of a unit root against the alternative—that the series of interest follows a globally stationary exponential smooth transition autoregressive process. The response surface models allow estimation of finite-sample critical values and approximate p-values for different combinations of the number of observations, T, and the lag order in the test regression, p. The latter can be either specified by the user or optimally selected using a data-dependent procedure. We present the new commands kssur and ksur and illustrate their use with an empirical example.
Technical Financial Analysis Tools for Stata
Authors: Dicle Mehmet F. , Levendis John D.
Published: November 2018

In this article, we provide four financial technical analysis tools: moving averages, Bollinger bands, moving-average convergence divergence, and the relative strength index. The tftools command is used with four subcommands, each referring to a technical analysis tool: bollingerbands, macd, movingaverage, and rsi. We provide examples for each tool. tftools allows researchers to backtest their own investment strategies and will be of interest to investors, researchers, and students of finance.
Power and sample-size analysis for the Royston–Parmar combined test in clinical trials with a time-to-event outcome
Authors: Royston Patrick
Published: November 2018

Randomized controlled trials with a time-to-event outcome are usually designed and analyzed assuming proportional hazards (PH) of the treatment effect. The sample-size calculation is based on a log-rank test or the nearly identical Cox test, henceforth called the Cox/log-rank test. Nonproportional hazards (non-PH) has become more common in trials and is recognized as a potential threat to interpreting the trial treatment effect and the power of the log-rank test—hence to the success of the trial. To address the issue, in 2016, Royston and Parmar ( BMC Medical Research Methodology 16: 16) proposed a “combined test” of the global null hypothesis of identical survival curves in each trial arm. The Cox/log-rank test is combined with a new test derived from the maximal standardized difference in restricted mean survival time (RMST) between the trial arms. The test statistic is based on evaluations of the between-arm difference in RMST over several preselected time points. The combined test involves the minimum p-value across the Cox/log-rank and RMST-based tests, appropriately standardized to have the correct distribution under the global null hypothesis. In this article, I introduce a new command, power_ct, that uses simulation to implement power and sample-size calculations for the combined test. power_ct supports designs with PH or non-PH of the treatment effect. I provide examples in which the power of the combined test is compared with that of the Cox/log-rank test under PH and non-PH scenarios. I conclude by offering guidance for sample-size calculations in time-to-event trials to allow for possible non-PH.
Testing for Serial Correlation in Fixed-effects Panel Models
Authors: Wursten Jesse
Published: November 2018

Current serial correlation tests for panel models are cumbersome to use, not suited for fixed-effects models, or limited to first-order autocorrelation. To fill this gap, I implement three recently developed tests.
Some Commands to Help Produce Rich Text Files from Stata
Authors: Gillman Matthew S.
Published: November 2018

Producing Rich Text Format (RTF) files from Stata can be difficult and somewhat cryptic. In this article, I introduce commands to simplify this process; one builds a table row by row, another inserts a PNG image file into an RTF document, and the others start and finish the RTF document.
Standard-error Correction in Two-stage Optimization Models: A Quasi–maximum Likelihood Estimation Approach
Authors: Rios-Avila Fernando , Canavire-Bacarreza Gustavo
Published: November 2018

Following Wooldridge (2014, Journal of Econometrics 182: 226–234), we discuss and implement in Stata an efficient maximum-likelihood approach to the estimation of corrected standard errors of two-stage optimization models. Specifically, we compare the robustness and efficiency of the proposed method with routines already implemented in Stata to deal with selection and endogeneity problems. This strategy is an alternative to the use of bootstrap methods and has the advantage that it can be easily applied for the estimation of two-stage optimization models for which already built-in programs are not yet available. It could be of particular use for addressing endogeneity in a nonlinear framework.
Manipulation Testing Based on Density Discontinuity
Authors: Cattaneo Matias D. , Jansson Michael , Ma Xinwei
Published: November 2018

In this article, we introduce two community-contributed commands, rddensity and rdbwdensity, that implement automatic manipulation tests based on density discontinuity and are constructed using the results for local-polynomial density estimators in Cattaneo, Jansson, and Ma (2017b, Simple local polynomial density estimators, Working paper, University of Michigan). These new tests exhibit better size properties (and more power under additional assumptions) than other conventional approaches currently available in the literature. The first command, rddensity, implements manipulation tests based on a novel local-polynomial density estimation technique that avoids prebinning of the data (improving size properties) and allows for restrictions on other features of the model (improving power properties). The second command, rdbwdensity, implements several bandwidth selectors specifically tailored for the manipulation tests discussed herein. We also provide a companion R package with the same syntax and capabilities as rddensity and rdbwdensity.
Inference for Clustered Data
Authors: Lee Chang Hyung , Steigerwald Douglas G.
Published: November 2018

In this article, we introduce clusteff, a community-contributed command for checking the severity of cluster heterogeneity in cluster–robust analyses. Cluster heterogeneity can cause a size distortion leading to underrejection of the null hypothesis. Carter, Schnepel, and Steigerwald (2017, Review of Economics and Statistics 99: 698–709) develop the effective number of clusters to reflect a reduction in the degrees of freedom, thereby mirroring the distortion caused by assuming homogeneous clusters. clusteff generates the effective number of clusters. We provide a decision tree for cluster–robust analysis, demonstrate the use of clusteff, and recommend methods to minimize the size distortion.
Frailty Models and Frailty-mixture Models for Recurrent Event Times: Update
Authors: Xu Ying , Cheung Yin Bun
Published: November 2018

Xu and Cheung (2015, Stata Journal 15: 135–154) introduced the strmcure command, which fits frailty models and frailty-mixture models in the analysis of recurrent event times. In this article, we provide an update to strmcure. The update implements a two-step estimation procedure for a frailty-mixture model that allows the estimation of the effect of an intervention on the probability of cure and on the total effect on event rate in the noncured. To illustrate, we will use the same example dataset on respiratory exacerbations from the original article.
Review of Tenko Raykov and George Marcoulides's a Course in Item Response Theory and Modeling with Stata
Authors: Linden Ariel
Published: November 2018

In this article, I review A Course in Item Response Theory and Modeling with Stata by Tenko Raykov and George A. Marcoulides (2018 [Stata Press]).
Experimental study on the effect of high-pressure and low-pressure exhaust gas recirculation on gasoline engine and turbocharger
Authors: Shen Xianqing , Shen Kai , Zhang Zhendong
Published: November 2018

The effects of high-pressure and low-pressure exhaust gas recirculation on engine and turbocharger performance were investigated in a turbocharged gasoline direct injection engine. Some performances, such as engine combustion, fuel consumption, intake and exhaust, and turbocharger operating conditions, were compared at wide open throttle and partial load with the high-pressure and low-pressure exhaust gas recirculation systems. The reasons for these changes are analyzed. The results showed EGR system of gasoline engine could optimize the cylinder combustion, reduce pumping mean effective pressure and lower fuel consumption. Low-pressure exhaust gas recirculation system has higher thermal efficiency than high-pressure exhaust gas recirculation, especially on partial load condition. The main reasons are as follows: more exhaust energy is used by the turbocharger with low-pressure exhaust gas recirculation system, and the lower exhaust gas temperature of engine would optimize the combustion in cylinder.
Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle
Authors: Shi QIN , Qiu Duoyang , He Lin , Wu Bing , Li Yiming
Published: November 2018

For a great influence on the fuel economy and exhaust, driving cycle recognition is becoming more and more widely used in hybrid electric vehicles. The purpose of this study is to develop a method to identify the type of driving cycle in real time with better accuracy and apply the driving cycle recognition to minimize the fuel consumption with dynamic equivalent fuel consumption minimization strategy. The support vector machine optimized by the particle swarm algorithm is created for building driving cycle recognition model. Furthermore,the influence of the two parameters of window width and window moving velocity on the accuracy is also analyzed in online application. A case study of driving cycle in a medium-sized city is introduced based on collecting four typical driving cycle data in real vehicle test. A series of characteristic parameters are defined and principal component analysis is used for data processing. Finally, the driving cycle recognition model is used for equivalent fuel consumption minimization strategy with a parallel hybrid electric vehicle. Simulation results show that the fuel economy can improve by 9.914% based on optimized support vector machine, and the fluctuations of battery state of charge are more stable so that system efficiency and batter life are substantially improved.
Temperature effect on icing distribution near blade tip of large-scale horizontal-axis wind turbine by numerical simulation
Authors: Li Yan , Sun Ce , Jiang Yu , Yi Xian , Xu Zhi , Guo Wenfeng
Published: November 2018

To investigate the temperature effect on characteristics of icing distribution near the tip part of rotating blade of large-scale horizontal-axis wind turbine, numerical simulations were carried out on a horizontal-axis wind turbine rotor with rated power of 1.5 MW based on a quasi-three-dimensional computational method developed in this study. The icing simulation was focused on the part between blade tip and 30% length of blade from tip along span wise to blade root where the most serious icing area is according to the past researches. Eight sections along blade were selected, and the ice accretion on each airfoil was calculated. Eight temperature values from −6°C to −20°C were decided to investigate the effects of temperature on icing under the certain liquid water content and medium volume droplet. Three icing times were selected to research the ice accretion on blade surface with the increase in the time. According to the results, the icing distribution has the overall characteristics that the icing shape changes from horn icing shape to streamline icing shape with decrease in the temperature. The closer the blade airfoil section to blade tip, the more obvious the ice accretion is. This study can be a reference for the research on anti-icing and de-icing technologies for large-scale horizontal-axis wind turbine.
An investigation of the conceptualization of peace and war in peace journalism studies of media coverage of national and international conflicts
Authors: Gouse Valerie , Valentin-Llopis Mariely , Perry Stephen , Nyamwange Beryl
Published: November 2018

According to Galtung’s articles ‘On the role of the media in worldwide security and peace’ (1986) and ‘High road, low road: Charting the course for peace journalism’ (1998), war journalism and peace journalism are two competing frames when reporting news on war and conflict. War journalists reactively report on conflict in a way that propagates violence, victory, and an elitist orientation. On the contrary, peace journalists proactively report on the causes of and solutions to a conflict, giving voice to all parties through responsible, empathetic journalism. By searching databases for multiple examples of qualitative and quantitative literature on peace and war journalism, new paths to best practices of how scholars articulate and measure the concepts of peace and war using content analysis methods can be found. This article reports on studies published in peer-reviewed journals that investigate the attributes of peace and war as they are conceptualized by scholars analyzing newspaper articles, television broadcasts, and radio reports within the context of peace journalism. Results suggest the majority of peace journalism studies examine media surrounding direct violence as it is occurring, and assess it most often by using the war/peace indicator of elite-oriented versus people-oriented.
Effectiveness of healthcare educational and behavioral interventions to improve gout outcomes: a systematic review and meta-analysis
Authors: Ramsubeik Karishma , Ramrattan Laurie Ann , Kaeley Gurjit S. , Singh Jasvinder A.
Published: November 2018

Background:

We aimed to systematically review the effectiveness of healthcare behavioral and education interventions for gout patients on clinical outcomes.

Methods:

We searched multiple databases to identify trials or observational studies of educational or behavioral interventions in gout. Risk of bias was assessed with the Cochrane tool for randomized control trials (RCTs) and the Newcastle–Ottawa Scale for observational studies. We estimated odds ratios (ORs) for categorical and standardized mean difference (SMD) for continuous measures using a random-effects model.

Results:

Overall, eight (five RCTs and three observational) studies met the inclusion criteria and examined pharmacist-led interventions ( n = 3), nurse-led interventions ( n = 3) and primary care provider interventions ( n = 2). Compared with the control intervention (usual care in most cases), a higher proportion of those in the educational/behavioral intervention arm achieved serum urate (SU) levels <6 mg/dl, 47.2% versus 23.8%, the OR was 4.86 [95% confidence interval (CI), 1.48, 15.97; 4 RCTs] with moderate quality evidence. Compared with the control intervention, a higher proportion of those in the educational/behavioral intervention arm were adherent to allopurinol, achieved at least a 2 mg/dl decrease in SU, achieved an SU < 5 mg/dl, had a reduction in the presence of tophi at 2 years, had improved quality of life as assessed with SF-36 physical component scores, had a higher knowledge about gout and higher patient satisfaction (moderate-low quality evidence).

Conclusion:

Educational and behavioral interventions can improve gout outcomes in the short-intermediate term. Randomized trials are needed to assess its impact on long-term gout outcomes.