Template-Type: ReDIF-Article 1.0 Author-Name: Nicholas J. Cox Author-Workplace-Name: Durham University Author-Person: pco34 Author-Name: Stephen P. Jenkins Author-Workplace-Name: London School of Economics Author-Email: s.jenkins@lse.ac.uk Author-Person: pje7 Title: The Stata Journal Editors’ Prize 2023: Fernando Rios-Avila Journal: Stata Journal Pages: 905-908 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212425 File-URL: http://www.stata-journal.com/article.html?article=gn0096 File-Function: link to article purchase Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:905-908 Template-Type: ReDIF-Article 1.0 Author-Name: Achim Ahrens Author-Workplace-Name: ETH Zürich Author-Email: achim.ahrens@gess.ethz.ch Author-Person: pah173 Author-Name: Christian B. Hansen Author-Workplace-Name: University of Chicago Author-Email: christian.hansen@chicagobooth.edu Author-Person: pha982 Author-Name: Mark E. Schaffer Author-Workplace-Name: Heriot-Watt University Author-Email: m.e.schaffer@hw.ac.uk Author-Person: psc51 Title: pystacked: Stacking generalization and machine learning in Stata Journal: Stata Journal Pages: 909-931 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212426 Abstract: The pystacked command implements stacked generalization (Wolpert, 1992, Neural Networks 5: 241–259) for regression and binary classification via Python’s scikit-learn. Stacking combines multiple supervised machine learners— the “base” or “level-0” learners—into one learner. The currently supported base learners include regularized regression, random forest, gradient boosted trees, support vector machines, and feed-forward neural nets (multilayer perceptron). pystacked can also be used as a “regular” machine learning program to fit one base learner and thus provides an easy-to-use application programming interface for scikit-learn’s machine learning algorithms. Keywords: pystacked, machine learning, stacked generalization, model averaging, Python, sci-kit learn File-URL: http://www.stata-journal.com/article.html?article=st0731 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-4/st0731/ Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:909-931 Template-Type: ReDIF-Article 1.0 Author-Name: Seojeong Lee Author-Workplace-Name: Seoul National University Author-Email: s.jay.lee@snu.ac.kr Author-Person: ple681 Author-Name: Siha Lee Author-Workplace-Name: McMaster University Author-Email: lees223@mcmaster.ca Author-Person: ple1090 Author-Name: Julius Owusu Author-Workplace-Name: McMaster University Author-Email: owusuj4@mcmaster.ca Author-Name: Youngki Shin Author-Workplace-Name: McMaster University Author-Email: shiny11@mcmaster.ca Author-Person: psh180 Title: csa2sls: A complete subset approach for many instruments using Stata Journal: Stata Journal Pages: 932-941 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212432 Abstract: We developed a command, csa2sls, that implements the complete sub- set averaging two-stage least-squares (CSA2SLS) estimator in Lee and Shin (2021, Econometrics Journal 24: 290–314). The CSA2SLS estimator is an alternative to the two-stage least-squares estimator that remedies the bias issue caused by many correlated instruments. We conduct Monte Carlo simulations and confirm that the CSA2SLS estimator reduces both the mean squared error and the estimation bias substantially when instruments are correlated. We illustrate the usage of csa2sls in Stata with an empirical application. Keywords: csa2sls, many instruments, complete subset averaging, two-stage least squares File-URL: http://www.stata-journal.com/article.html?article=st0732 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-4/st0732/ Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:932-941 Template-Type: ReDIF-Article 1.0 Author-Name: James G. MacKinnon Author-Workplace-Name: Queen’s University Author-Email: mackinno@queensu.ca Author-Person: pma63 Author-Name: Morten Ørregaard Nielsen Author-Workplace-Name: Aarhus University Author-Email: mon@econ.au.dk Author-Person: pni42 Author-Name: Matthew D. Webb Author-Workplace-Name: Carleton University Author-Email: matt.webb@carleton.ca Author-Person: pwe297 Title: Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust Journal: Stata Journal Pages: 942-982 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212433 Abstract: We introduce a new command, summclust, that summarizes the cluster structure of the dataset for linear regression models with clustered disturbances. The key unit of observation for such a model is the cluster. We therefore propose cluster-level measures of leverage, partial leverage, and influence and show how to compute them quickly in most cases. The measures of leverage and partial leverage can be used as diagnostic tools to identify datasets and regression designs in which cluster–robust inference is likely to be challenging. The measures of influence can provide valuable information about how the results depend on the data in the various clusters. We also show how to calculate two jackknife variance matrix estimators efficiently as a by-product of our other computations. These estimators, which are already available in Stata, are generally more conservative than conventional variance matrix estimators. The summclust command computes all the quantities that we discuss. Keywords: summclust, clustered data, cluster–robust variance estimator, CRVE, grouped data, high-leverage clusters, influential clusters, jackknife, partial leverage, robust inference File-URL: http://www.stata-journal.com/article.html?article=st0733 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-4/st0733/ Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:942-982 Template-Type: ReDIF-Article 1.0 Author-Name: Sebastian Kripfganz Author-WorkPlace-Name: University of Exeter Business School Author-Email: S.Kripfganz@exeter.ac.uk Author-Person: pkr246 Author-Name: Daniel C. Schneider Author-WorkPlace-Name: Max Planck Institute for Demographic Research Author-Email: schneider@demogr.mpg.de Title: ardl: Estimating autoregressive distributed lag and equilibrium correction models Journal: Stata Journal Pages: 983-1019 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212434 Abstract: We present a command, ardl, for the estimation of autoregressive distributed lag (ARDL) models in a time-series context. The ardl command can be used to fit an ARDL model with the optimal number of autoregressive and distributed lags based on the Akaike or Bayesian (Schwarz) information criterion. The regression results can be displayed in the ARDL levels form or in the error- correction representation of the model. The latter separates long-run and short-run effects and is available in two different parameterizations of the long-run (cointe- grating) relationship. The popular bounds-testing procedure for the existence of a long-run levels relationship is implemented as a postestimation feature. Compre- hensive critical values and approximate p-values obtained from response-surface regressions facilitate statistical inference. Keywords: ardl, ardl postestimation, autoregressive distributed lag model, error-correction model, bounds test, long-run relationship, cointegration, time-series data File-URL: http://www.stata-journal.com/article.html?article=st0734 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-4/st0734/ Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:983-1019 Template-Type: ReDIF-Article 1.0 Author-Name: Farahnaz Islam Author-Workplace-Name: Tempus Labs Author-Email: farahnaz.islam@tempus.com Author-Name: James F. Thrasher Author-Workplace-Name: University of South Carolina Author-Email: thrasher@mailbox.sc.edu Author-Name: Feifei Xiao Author-Workplace-Name: University of Florida Author-Email: feifeixiao@ufl.edu Author-Name: Robert R. Moran Author-Workplace-Name: University of South Carolina Author-Email: rrmoran@mailbox.sc.edu Author-Name: James W. Hardin Author-Workplace-Name: University of South Carolina Author-Email: jhardin@sc.edu Title: Data management and techniques for best–worst discrete choice experiments Journal: Stata Journal Pages: 1020-1044 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212437 Abstract: In this article, we present software that is suitable for use with Stata’s choice modeling suite of commands, which begin with cm. Within the context of choice models, we focus on best–worst data. In such data, respondents are presented a set of choices and are required to select a best and a worst choice from among the alternatives. Optionally, respondents may indicate an opt-out choice, in which no best or worst choice exists in the choice set. Such data are simplified versions of experiments in which respondents rank all the choices. Once best– worst data are collected, there are specific types of data expansions that analysts use to take advantage of both explicit and implicit information. The commands described in this article support data expansion and model estimation. Keywords: cm_expand, cm_bwpairs, cm_bwsumm, cm_bestworst, choice models, postestimation, attributes, discrete choice experiments, best–worst, maxdiff choice models File-URL: http://www.stata-journal.com/article.html?article=st0735 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-1/st0735/ Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:1020-1044 Template-Type: ReDIF-Article 1.0 Author-Name: Daniele Spinelli Author-Email: daniele.spinelli@unimib.it Author-WorkPlace-Name: University of Milano–Bicocca Author-Person: psp173 Title: Improving flexibility and ease of matrix subsetting: The submatrix command Journal: Stata Journal Pages: 1045-1056 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X221141012 Abstract: Matrix manipulation in Stata can be a time-consuming and tedious task, especially when it is necessary to subset or rearrange elements from large matrices based on nonconsecutive elements. Compared with Mata, these tasks require more time, more code, and sometimes more complex output. The purpose of this article is to introduce submatrix, a command to manipulate matrix elements using row (and column) names, numbers, and equations. Keywords: submatrix, row names, column names, permutation vectors File-URL: http://www.stata-journal.com/article.html?article=pr0077 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-4/pr0077/ Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:1045-1056 Template-Type: ReDIF-Article 1.0 Author-Name: Joseph V. Terza Author-Email: jvterza@iupui.edu Author-WorkPlace-Name: Indiana University–Purdue University Indianapolis Author-Person: pte168 Title: Simpler standard errors for two-stage optimization estimators revisited Journal: Stata Journal Pages: 1057-1061 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212445 Abstract: “Simpler standard errors for two-stage optimization estimators” (Terza, 2016a, Stata Journal 16: 368–385) offers an analytic simplification of the daunting textbook formulations of the asymptotic variance–covariance matrix of a class of two-stage optimization estimators. Here I revisit that simplification and show that it applies to a much broader class of estimators than was originally considered. I also offer a correction that further enhances the generality of this asymptotic variance–covariance matrix formulation. These points are illustrated via a real-data application. Keywords: two-stage optimization estimators, standard errors, asymptotic theory, endogeneity, two-stage residual inclusion, sandwich estimator File-URL: http://www.stata-journal.com/article.html?article=st0696 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-4/st0696/ Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:1057-1061 Template-Type: ReDIF-Article 1.0 Author-Name: Sebastian Kripfganz Author-WorkPlace-Name: University of Exeter Business School Author-Email: S.Kripfganz@exeter.ac.uk Author-Person: pkr246 Title: Review of A. Colin Cameron and Pravin K. Trivedi’s Microeconometrics Using Stata, Second Edition Journal: Stata Journal Pages: 1062-1073 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212446 Abstract: In this article, I review Microeconometrics Using Stata, Second Edition, by A. Colin Cameron and Pravin K. Trivedi (2022, Stata Press). Keywords: book review, regression, cross-sectional data, panel data, non-linear models, causal inference, Stata File-URL: http://www.stata-journal.com/article.html?article=gn0097 File-Function: link to article purchase Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:1062-1073 Template-Type: ReDIF-Article 1.0 Author-Name: Andrew Musau Author-WorkPlace-Name: Molde University College Author-Email: amus@himolde.no Author-Person: pmu556 Title: Review of Alan Acock’s A Gentle Introduction to Stata, Revised Sixth Edition Journal: Stata Journal Pages: 1074-1081 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X221141021 Abstract: In this article, I review A Gentle Introduction to Stata, Revised Sixth Edition, by Alan Acock (2023, Stata Press). Keywords: book review, introduction to Stata, teaching statistics, behavioral and social sciences, Stata File-URL: http://www.stata-journal.com/article.html?article=gn0098 File-Function: link to article purchase Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:1074-1081 Template-Type: ReDIF-Article 1.0 Author-Name: Tim Collier Author-WorkPlace-Name: London School of Hygiene and Tropical Medicine Author-Email: tim.collier@lshtm.ac.uk Title: An Introduction to Stata for Health Researchers review Journal: Stata Journal Pages: 1082-1085 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212452 Abstract: In this article, I review An Introduction to Stata for Health Researchers, by Svend Juul and Morten Frydenberg (2021, Stata Press). Keywords: book review, introduction to Stata, data management, statistical analysis, health research File-URL: http://www.stata-journal.com/article.html?article=gn0099 File-Function: link to article purchase Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:1082-1085 Template-Type: ReDIF-Article 1.0 Author-Name: Nicholas J. Cox Author-Workplace-Name: Durham University Author-Email: n.j.cox@durham.ac.uk Author-Person: pco34 Title: Speaking Stata: Finding the denominator: Minimum sample size from percentages Journal: Stata Journal Pages: 1086-1095 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212453 Abstract: Percentage breakdowns for a series of classes or categories are sometimes reported without a specification of class frequencies or even the total sample size. This column surveys the problem of estimating the minimum sample size and class frequencies consistent with a reported breakdown and a particular resolution. I introduce and explain a new command, find_denom. Rounding quirks whereby a total is reported as above or below 100% are discussed as a complication. Keywords: find_denom, sample size, percentages, resolution, rounding, chi-squared tests, data checking, data quality File-URL: http://www.stata-journal.com/article.html?article=gr0092 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-4/gr0092/ Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:1086-1095 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 1096 Issue: 4 Volume: 23 Year: 2023 Month: December DOI: 10.1177/1536867X231212454 Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-4/dm0042_5/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-4/dm0048_5/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-4/dm0092_2/ Note: Windows users should not attempt to download these files with a web browser. Handle:RePEc:tsj:stataj:v:23:y:2023:i:4:p:1096