Jerry Agulehi

7/14/2021 2021 Stata Conference | Stata » Home » Stata Conferences » 2021 Stata Conference 2021 Stata Conference August 5th & 6th, Virtual Register now THE STATA CONFERENCE VIEW PROGRAM SCIENTIFIC COMMITTEE – THE CONFERENCE – What is the Stata Conference? #Stata2021 comes to Seattle your home Whether you are a beginner or an expert, you will find something just for you at the Stata Conference. Connect with researchers using Stata from across all disciplines. Enjoy presentations by experienced Stata users and Stata developers. Make plans now, and join us for this unique opportunity to learn new ways of using Stata and to network with the Stata community. After carefully considering COVID-19–related restrictions and the risks of an in-person event, we have decided to move the Stata Conference online. A virtual format will provide the safest and most convenient method of attendance during these challenging times. Order some delivery, snap a picture from your couch, and enjoy two days of networking and Stata exploration. https://www.stata.com/meeting/us21/ 1/12 7/14/2021 2021 Stata Conference | Stata – THE PROGRAM – Day 1: Thursday, 5 August 2021 All times are in Pacific Time (UTC -07:00) 7:00–7:10 Welcome and introductions 7:10–8:50 Session 1 dstat: A new command for the analysis of distributions Abstract: In this talk, I will present a new Stata command that unites a variety of methods to describe (univariate) statistical distributions. Covered are density estimation, histograms, cumulative distribution functions, probability distributions, quantile functions, Lorenz curves, percentile shares, and a large collection of summary statistics, such as classical and robust measures of location, scale, skewness, and kurtosis, as well as inequality, concentration, and poverty measures. ...(Read more) Ben Jann University of Bern distcomp: Comparing distributions Abstract: I developed the distcomp command to help Stata users compare distributions using recent methodology from the econometrics literature (Goldman and Kaplan 2018; https://doi.org/10.1016/j.jeconom-). Goodness-of-fit tests like ksmirnov simply test the null hypothesis that two distributions are identical; the test can only reject or accept. Providing more information, distcomp identifies specific intervals on which the distributions' difference is statistically significant, while still controlling the false-positive rate appropriately. A secondary benefit of distcomp is its improved power in the tails compared with ksmirnov. David Kaplan University of Missouri Averaged shifted histograms (ASHs) or weighted averaging of rounded points (WARPs): Efficient methods to calculate kernel density estimators for circular data Abstract: By solving the histograms' problems of origin dependency and discontinuity and by having guidance to choose the best bandwidth and the feasibility of variable bandwidth procedures, the kernel density estimators (KDEs) are powerful tools to explore and analyze data distributions. However, an important drawback of these methods is that they require a considerable number of calculations, which may require a long time to obtain the result, even using fast processors and moderate sample sizes. A way to overcome this problem is through ASHs, a procedure later recognized as being a part of the more general procedure. ...(Read more) Isaías Hazarmabeth Salgado-Ugarte FES Zaragoza, Universidad Nacional Autónoma de México Beyond histograms and box plots: Some commands for univariate distribution graphics Abstract: Whatever we do in statistical science should be rooted in careful and comprehensive description and exploration of the data. This presentation surveys various commands by the author for plotting univariate distributions, without neglecting the need for concise and genuinely informative numerical summaries. Graphical highlights include qplot (SJ) and multqplot (SJ) for quantile plots, the complementary distplot (SJ) for (empirical [cumulative]) distribution plots, multidensity (SSC) for density function estimates, stripplot (SSC) for strip plots and much more, and transplot (SSC) for trying out transformations. ...(Read more) Nicholas J. Cox Durham University 8:50–9:00 https://www.stata.com/meeting/us21/ Break and presenter breakout rooms 2/12 7/14/2021 2021 Stata Conference | Stata 9:00–11:15 Session 2 StataCorp presentation: Customizable tables Abstract: Presenting results effectively is a crucial step in statistical analyses, and creating tables is an important part of this step. In this presentation, I will introduce two new features in Stata 17—the updated table command and the new collect suite—that you can use to create, customize, and export tables. With table, you can now easily create cross-tabulations, tables of summary statistics, tables of regression results, and more. With the collect suite, you can create and customize tables of results returned by any Stata command. ...(Read more) Kristin MacDonald StataCorp Incorporating Stata into reproducible documents during survey data management for intervention projects in Africa Abstract: The introduction of Stata 15 commands like putdocx and several communitycontributed commands has enabled the automation and use of other softwares (QGIS, Microsoft Word) for nontechnical data users to get a quick report and analysis of field data. The automation of the process and the reproducibility of the customized report is of great essence. Jerry Agulehi One Acre Fund Nigeria xtbreak: Estimation and tests for structural breaks in time series and panel data Abstract: The recent events that have plagued the global economy, such as the 2008 financial crisis or the 2020 COVID-19 outbreak, hint to multiple structural breaks in economic relationships. I present xtbreak, which implements the estimation of single and multiple break points and testing for structural breaks in time series and panel data. The estimation and the tests follow the methodologies developed in Andrews (1993, Econometrica), Bai and Perron (1998, Econometrica), and Ditzen, Karavias, and Westerlund (2021). ...(Read more) Jan Ditzen Free University of Bolzano-Bozen An LM test for the mean stationarity assumption in dynamic panel-data models Abstract: I present the new Stata command xttestms for computation of the LM test for verifying the assumptions underlying the system GMM estimation in the context of dynamic panel-data models. The test has been proposed by Magazzini and Calzolari (2020), who show its better performance with respect to testing procedures customarily employed in empirical research (that is, the Sargan/Hansen test checking the whole set of moment conditions of the system GMM approach and the difference-in-Sargan/Hansen, which compares the value of the minimized criterion function of the system and difference GMM approaches). ...(Read more) Laura Magazzini Institute of Economics, Sant'Anna School of Advanced Studies 11:15–11:25 https://www.stata.com/meeting/us21/ Break and presenter breakout rooms 3/12 7/14/2021 2021 Stata Conference | Stata 11:25–12:35 Session 3 One weird trick for better inference in experimental designs Abstract: A long line of research debates the merits of statistical adjustment for baseline or pretreatment characteristics for random assignment designs (Fisher 1935; Freedman 2008a, 2008b; Lin 2013; Kallus 2018). A related literature explores better methods to conduct statistical adjustment for potential confounders in nonexperimental designs. This presentation presents the results of a simulation showing large potential improvements in inference for random assignment designs attainable using commands designed to adjust for potential confounders newly available in Stata 16. Austin Nichols Abt Associates A “CACE” in point: Estimating causal effects via a latent class approach in RCTs with noncompliance using Stata Abstract: In randomized control trials (RCT), intention-to-treat (ITT) analysis is customarily used to estimate the effect of the trial; however, in the presence of noncompliance, this can often lead to biased estimates because ITT completely ignores varying levels of actual treatment received. This is a known issue that can be overcome by adopting the complier average causal effect (CACE) approach, which estimates the effect the trial had on the individuals who complied with the protocol. ...(Read more) Patricio Troncoso Heriot-Watt University Using the package hettreatreg to interpret OLS estimates under treatmenteffect heterogeneity Abstract: This presentation describes hettreatreg, a Stata package to compute diagnostics for linear regression when treatment effects are heterogeneous. Following my recent paper, "Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights" (forthcoming, Review of Economics and Statistics), every OLS estimate of the coefficient on a binary variable ("treatment") in a linear model with additive effects can be represented as a weighted average of two other estimates, corresponding to average treatment effects on the treated (ATT) and untreated (ATU). ...(Read more) Tymon Sloczynski Brandeis University 12:35–12:45 https://www.stata.com/meeting/us21/ Break and presenter breakout rooms 4/12 7/14/2021 2021 Stata Conference | Stata 12:45–2:25 Session 4 StataCorp presentation: Estimation, inference, and diagnostics for difference in differences Abstract: Stata 17 introduced two commands to fit difference-in-differences (DID) and difference-in-differences-in-differences (DDD) models. One of the commands is applicable to repeated cross-sectional data, didregress, and the other to panel/longitudinal data, xtdidregress. I will talk briefly about the theory behind DID and DDD models and then show how to fit the models by using the new commands. I will spend some time discussing the standard errors that are appropriate to use in different scenarios. I also discuss graphical diagnostics and tests that are relevant for DID and DDD specifications. Finally, I discuss new areas of development in the DID literature. Enrique Pinzon StataCorp Estimation of average treatment effects in staggered difference-in-differences designs Abstract: In this presentation, I discuss the att_gt command, which implements three semiparametric estimators for a family of average treatment-effects parameters in difference-indifferences (DID) setups with multiple time periods discussed in Callaway and Sant’Anna (2020, https://doi.org/10.1016/j.jeconom-). The first estimator models the outcome evolution of the comparison group, the second is a properly reweighted inverse-probability weighted estimator, and the third is a doubly robust estimator that relies on less stringent modeling assumptions. ...(Read more) Pedro Sant'Anna Vanderbilt University Trusting difference-in-difference estimates more: An approximate permutation test Abstract: Researchers use difference-in-differences models to evaluate the causal effects of policy changes. Because the empirical correlation across firms and time can be ambiguous, estimating consistent standard errors is difficult, and statistical inferences may be biased. I apply an approximate permutation test using simulated interventions to reveal the empirical error distribution of estimated policy effects. In contrast to existing econometric corrections, such as single or double clustering, this approach does not impose a specific parametric form on the residuals. In comparison with alternative parametric tests, this procedure maintains correct size with simulated and real-world interventions. Simultaneously, it improves power. Sebastian Bunnenberg Reutlingen University, ESB Business School allsynth: Synthetic control bias-corrections utilities for Stata Abstract: The synthetic control method has become a widely adopted empirical approach for estimating counterfactuals and treatment effects. The synth module written for Stata (Abadie, Diamond, and Hainmueller 2010) is widely used by practitioners and serves as the foundation for the synth_runner utilities package (Galiani and Quistorff 2018), which enhances functionality. An active literature has proposed numerous modifications to the "classic" approach, including a bias-correction procedure (Abadie and L'Hour 2020), analogous to that in Abadie and Imbens (2011) for matching estimators, to remove bias that results from differences in the predictor variables between a treated unit and its synthetic control donors. allsynth adds functionality to the synth module, which implements this bias-correction procedure and automates extension of the procedure to placebo runs for in-space randomization inference and graphing. Justin Wiltshire University of California, Davis 2:25–2:35 https://www.stata.com/meeting/us21/ Break and presenter breakout rooms 5/12 7/14/2021 2021 Stata Conference | Stata 2:35–4:35 Session 5 netivreg: Estimation of peer effects in endogenous social networks Abstract: I present the netivreg command, which implements the generalized three-stage leastsquares (G3SLS) estimator for the endogenous linear-in-means model developed in Estrada et al. (2020, “On the Identification and Estimation of Endogenous Peer Effects in Multiplex Networks"). The G3SLS procedure utilizes full observability of a two-layered multiplex network data structure using Stata 16's new multiframes capabilities and Python integration. Implementations of the command utilizing simulated data as well as three years' worth of data on peer-reviewed articles published in top general-interest journals in economics in Estrada et al. (2020) are also included. Juan Estrada Emory University Network regressions in Stata Abstract: Network analysis has become critical to the study of social sciences. While several Stata programs are available for analyzing network structures, programs that execute regression analysis with a network structure are currently lacking. We fill this gap by introducing the nwxtregress command. Building on spatial econometric methods (LeSage and Pace 2009), nwxtregress uses MCMC estimation to produce estimates of endogenous peer effects, as well as own-node (direct) and cross-node (indirect) partial effects, where nodes correspond to crosssectional units of observation, such as firms, and edges correspond to the relations between nodes. ...(Read more) Morad Zekhnini Michigan State University glasso: Graphical lasso for learning sparse inverse covariance matrices Abstract: In modern multivariate statistics, where high-dimensional datasets are ubiquitous, learning large inverse covariance matrices is a fundamental problem. A popular approach is to apply a penalty on the Gaussian log-likelihood and solve the convex optimization problem. Graphical lasso (Glasso) (Friedman et al. 2008) is one of the efficient and popular algorithms for imposing sparsity on the inverse covariance matrix. In this article, we introduce a corresponding new command glasso and explore the details of the algorithm. Moreover, we discuss widely used criteria for tuning parameter selection, such as the extended Bayesian information criterion (eBIC) and cross-validation (CV), and introduce corresponding commands. Simulation results and real data analysis illustrate the use of the Glasso. Aramayis Dallakyan Texas A&M University Validating a user-developed bivariate pseudo-random vector generator Abstract: Testing based on simulated data is an important component of the design and assessment of a newly developed estimation method. Often, the relevant modeling context involves bivariate outcomes, for example, endogenous treatment-effect (ETE) models and nonlinear seemingly unrelated regressions (SUR). Stata offers reliable commands for univariate pseudo-random-number generators for a wide variety of probability distributions but, as is the case for all statistical software packages, does not provide similar commands for bivariate pseudodata simulation. ...(Read more) Joseph Terza IUPUI 4:35–4:45 Break and presenter breakout rooms 4:45–5:45 Happy hour! Relax and chat with fellow Stata users. Day 2: Friday, 6 August 2021 All times are in Pacific Time (UTC -07:00) 7:00–7:10 https://www.stata.com/meeting/us21/ Welcome and introductions 6/12 7/14/2021 2021 Stata Conference | Stata 7:10–9:00 Session 6 Hunting for the missing score functions Abstract: Specific econometric models—such as the Cox regression, conditional logistic regression, and panel-data models—have likelihood functions that do not meet the so-called linear-form requirement. That means that the model's overall log-likelihood function does not correspond to the sum of each observation's log-likelihood contribution. Stata's ml command can fit said models using a particular group of evaluators: the d-family evaluators. Unfortunately, they have some limitations; one is that we cannot directly produce the score functions from the postestimation command predict. ...(Read more) Álvaro A. Gutiérrez-Vargas Research Centre for Operations Research and Statistics, KU Leuven rbprobit: Recursive bivariate probit estimation and decomposition of marginal effects Abstract: This article describes a new Stata command, rbprobit, for fitting recursive bivariate probit models, which differ from bivariate probit models in allowing the first dependent variable to appear on the right-hand side of the second dependent variable. Although the estimation of model parameters does not differ from the bivariate case, the existing commands biprobit and cmp do not consider the structural model’s recursive nature for postestimation commands. rbprobit estimates the model parameters, computes treatment effects of the first dependent variable, and gives the marginal effects of independent variables. ...(Read more) Mustafa Coban Institute for Employment Research (IAB) Estimation of ordered probit model with endogenous switching between two latent regimes Abstract: Ordinal responses can be generated, in the time-series context, by different latent regimes or, in the cross-sectional context, by different unobserved groups of population. These latent classes or states can distort the inference in a traditional single-equation model. Finite mixture or regime switching models surmount the problem of unobserved heterogeneity or clustering through their flexible form. The available Stata command for finite mixture of ordered probit models, fmm: oprobit, does not allow for endogenous switching, when the unobservables in the switching equation are correlated with the unobservables in the outcome equations. We introduce two new commands, swopit and swopitc, that fit a switching ordered probit model for ordered choices with exogenous and endogenous switching between two unobserved regimes or groups. ...(Read more) Jan Willem Nijenhuis University of Amsterdam Estimating the accuracy and consistency of classifications based on item response theory measures Abstract: Latent variables are used in economics to represent measures that influence the behavior or capture the traits of economic agents. Inference with latent variables often requires classifying individuals based on estimates of these variables to make analyses more tractable and easier to convey one’s findings to a wider audience. While classifying individuals is often straightforward, requiring estimates of their latent variables and their corresponding standard errors, and cutpoints, relatively few instruments are without measurement error. In many cases, this measurement error is transferred onto the estimates of individuals’ latent variables, which may result in individuals being misclassified (Rudner 2001; Lee 2020; Lathrop 2015). ...(Read more) Matthew P. Rabbitt U.S. Department of Agriculture Censored demand system estimation Abstract: We introduce the command quaidsce, a modified version of the estimation command provided by Poi (2008) to estimate the Almost Ideal Demand System proposed by Deaton and Muellbauer (1980) and extended by Banks et al. (1997) to allow for nonlinear demographic effects (through a price deflator for total expenditure) and nonlinear Engel curves (through a quadratic term of total expenditure). The command of Poi (2008) to estimate the Quadratic Almost Ideal Demand System (QUAIDS) is extended to a two-step censoring demand system. Postestimation tools calculate expenditure and price elasticities. Grace Melo University of Chile 9:00–9:10 https://www.stata.com/meeting/us21/ Break and presenter breakout rooms 7/12 7/14/2021 2021 Stata Conference | Stata 9:10–11:00 Session 7 Making Stata estimation commands faster through automatic differentiation and integration with Python Abstract: Fitting complex statistical models to very large datasets can be frustratingly slow. This is particularly problematic if multiple models need to be fit, for example, when using bootstrapping, cross-validation, or multiple imputation. I will introduce the mlad command, as an alternative to Stata's ml command, to estimate parameters using maximum likelihood. Rather than writing a Stata or Mata function to calculate the likelihood, mlad requires this to be written in Python. ...(Read more) Paul Lambert University of Leicester / Karolinska institutet Machine learning using Stata/Python Abstract: We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting popular machine learning (ML) methods in both regression and classification settings. Using the recent Stata/Python integration platform (sfi) of Stata 16, these commands provide hyperparameters' optimal tuning via K-fold cross-validation using greed search. More specifically, they make use of the Python Scikit-learn API to carry out both cross-validation and outcome/label prediction. Giovanni Cerulli IRCrES-CNR Beyond n-grams, tf-idf, and word indicators for text: Leveraging the Python API for vector embeddings Abstract: This talk will share strategies that Stata users can use to get more informative word, sentence, and document vector embeddings of text in their data. While indicator and bag-ofwords strategies can be useful for some types of text analytics, they lack the richness of the semantic relationships between words that provide meaning and structure to language. Vector space embeddings attempt to preserve these relationships and in doing so can provide more robust numerical representations of text data that can be used for subsequent analysis. I will share strategies for using existing tools from the Python ecosystem with Stata to leverage the advances in NLP in your Stata workflow. William Buchanan SAG Corporation A Stata 17 implementation of the local ratio autonomy: Calling Python Abstract: In many countries around the world, the public sector is decentralized to improve efficiency in the provision of public services. Until the publication of the paper by MartínezVazquez, Vulovic, and Liu (2011), the level of decentralization was approximated through the local income ratio. It has been shown that this covariate is endogenous and that because of the unobservable heterogeneity, it can generate correlation. The local autonomy ratio proposed by these authors is an indicator weighted by the inverse of the distance between municipalities, which in turn is weighted by the sum of the inverse of the distance between all municipalities in the country. ...(Read more) Juan S. Morales-Castillo University of Granada 11:00–11:10 Break and presenter breakout rooms 11:10–12:00 Open panel discussion with Stata developers https://www.stata.com/meeting/us21/ 8/12 7/14/2021 2021 Stata Conference | Stata 12:00–1:40 Session 8 StataCorp presentation: Bayesian econometrics in Stata 17 Abstract: Stata 17 introduced Bayesian support for many time-series and panel-data commands. In this talk, I will discuss Bayesian vector autoregression models, Bayesian DSGE models, and Bayesian panel-data models. Bayesian estimation is well suited to these models because economic considerations often impose structure that is captured well by informative priors. I will describe the main features of these commands as well as Bayesian diagnostics, posterior hypothesis tests, predictions, impulse–response functions, and forecasts. David Schenck StataCorp Use of the bayesmh command in Stata to calculate excess relative and excess absolute risk for radiation health risk estimates Abstract: Excess relative risk (ERR) and excess absolute risk (EAR) are important metrics typically used in radiation epidemiology studies. Most studies of long-term radiation effects in Japanese atomic bomb survivors feature Poisson regression of grouped survival data. Risks are modeled on the excess risk scale using linear and log-linear functions of regression parameters, which are generally formulated to produce both ERR and EAR as output. Given the specific assumptions underlying these models, they are dubbed ERR and EAR models, respectively. ... (Read more) Lori Chappell KBR Structural equation modeling the ICU patient microbiome and risk of bacteremia Abstract: Whether Candida interacts with Gram-positive bacteria to enhance their invasive potential from the microbiome leading to infection within intensive care unit (ICU) patients remains unclear. These infections arise from the colonizing flora, but quantifying microbial colonization in patients is not simple. Using published ICU infection prevention data, one can model the interaction between Candida and Gram-positive bacteria (at the level of the ICU) using generalized structural equation models (GSEM). In these models, colonization is a latent variable defined by the proportion of patients with respiratory tract or blood stream infections. ...(Read more) James Hurley University of Melbourne 1:40–1:50 Break and presenter breakout rooms 1:50–3:20 Session 9 Joint estimation of employment and unemployment hazard rates with unobserved heterogeneity using the hshaz2s command Abstract: In this talk, I present hshaz2s, a new Stata command that estimates two-states proportional-hazard rates models with unobserved heterogeneity specific to each of the two modeled states. hshaz2s uses the d2 ml method to provide the algebraic expressions of the first- and second-order derivatives of the log-likelihood function to achieve the model convergence faster, which is especially relevant for empirical researchers dealing with large longitudinal microdata sets. Results of fitting a discrete time duration model that jointly estimates the transition rates from employment and unemployment states on a sample of workers in the Spanish labor market are presented to show the main features of hshaz2s. David Troncoso-Ponce University of Seville Markup estimation using Stata: Micro and macro approaches with markupest Abstract: The dynamics of markups both at the firm and at the economy-wide level has recently attracted renewed attention from scholars, institutions, and broader audiences . In this presentation, I review the main methods for markup estimation both at the micro and at the macro level, and I provide methodological insights as a guide for applied researchers. Finally, I present a new Stata module (markupest) that implements all methods and an addition to the user-contributed module prodest, aimed at estimating markups after the estimation of a production function. I show their main features and performance both on example firm-level production datasets and on national accounts data and further strengthen the results through a series of Monte Carlo simulations. Gabriele Rovigatti Bank of Italy Measurement error and misclassification in linked earnings data: Estimation of the Kapteyn and Ypma model Abstract: Kapteyn and Ypma (KY; 2007, https://doi.org/10.1086/513298) is an influential study for the analysis of linked administrative and survey earnings data that was the first to allow for measurement errors in both sources of data. Allowing for measurement errors in administrative data, they find evidence that the oft-cited feature of mean-reversion errors in survey data virtually disappeared. ...(Read more) Fernando Rios-Avila Levy Economics Institute 3:20–3:30 https://www.stata.com/meeting/us21/ Break and presenter breakout rooms 9/12 7/14/2021 2021 Stata Conference | Stata 3:30–5:00 Session 10 Drivers of COVID-19 outcomes: Evidence from a heterogeneous SAR paneldata model Abstract: In an extension of the standard spatial autoregressive (SAR) model, Aquaro, Bailey and Pesaran (ABP; 2021, https://doi.org/10.1002/jae.2792) introduced a SAR panel model that allows one to produce heterogeneous point estimates for each spatial unit. Their methodology has been implemented as the Stata routine hetsar (Belotti, 2021, Statistical Sofware Components S458926). As the COVID-19 pandemic has evolved in the U.S. since its first outbreak in February 2020 with following resurgences of multiple widespread and severe waves of the pandemic, the level of interactions between geographic units (for example, states and counties) has differed greatly over time in terms of the prevalence of the disease. ...(Read more) Kit Baum Boston College Vaccination coverage quality indicators (VCQI): A flexible collection of Stata programs for standardized survey data analysis Abstract: Household surveys are a vital source of data on childhood vaccination in low- and middle-income countries. To evaluate effectiveness of government vaccination programs, the World Health Organization (WHO) has developed a set of outcome indicators that may be calculated from survey data. In this talk, I describe our collection of Stata programs—called Vaccination Coverage Quality Indicators (VCQI)—that WHO makes freely available so survey analysts can calculate those indicators in a consistent and transparent manner from surveys that can vary in many specific details. ...(Read more) Dale Rhoda Biostat Global Consulting Calprotectin, an emerging biomarker of interest in COVID-19: Meta-analysis using Stata Abstract: COVID-19 has been shown to present with a varied clinical course, hence the need for more specific diagnostic tools that could identify severe cases and predict outcomes during COVID-19 infection. Recent evidence has shown an expanded potential role for calprotectin, both as a diagnostic tool and as a stratifying tool in COVID-19 patients in terms of severity. Therefore, this systematic review and meta-analysis aims to evaluate the levels of calprotectin in severe and nonsevere COVID-19 and also identify the implication of raised calprotectin levels. ... (Read more) Raphael Udeh University of Newcastle, Australia 5:00–5:10 Presenter breakout rooms / Adjourn – REGISTER NOW – In light of this year's change to a virtual platform because of COVID-19, we are pleased to announce all proceeds from registrations for the 2021 Stata Conference will be donated to Feeding America. Special conference price Both days: 5–6 August 2021 $95 Register Registrations are limited, and you must register to attend, so register soon. The deadline for registration is 30 July 2021. We will send you an email prior to the start of the conference with instructions on how to access the meeting. Don't miss this opportunity. – SCIENTIFIC COMMITTEE – https://www.stata.com/meeting/us21/ 10/12 7/14/2021 2021 Stata Conference | Stata Austin Nichols Abt Associates (Chair) Sophia Rabe‐Hesketh University of California, Berkeley Phil Ender UCLA, ret. 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