XVIII Conferenza Italiana degli Utenti di Stata

L’incontro rappresenta un’ottima occasione per lo scambio interdisciplinare tramite la presentazione di nuovi comandi scritti dagli utenti e applicazioni sviluppate in Stata in diversi campi. Seguendo il formato ormai consolidato negli anni, la conferenza si aprirà con la sessione “Invited Speaker” incentrata sui più recenti sviluppi scritti dall’utente nella visualizzazione dei dati in Stata e proseguirà con una serie di sessioni che daranno la possibilità ai partecipanti di:

• INCONTRARE ricercatori appartenenti a diverse aree disciplinari
• CONOSCERE nuove applicazioni che evidenziano le potenzialità di Stata su nuovi comandi e procedure
• SCAMBIARE informazioni e nuove routine sviluppate per Stata e
• INTERAGIRE direttamente con gli statistici della StataCorp.

Quest’anno saranno particolarmente graditi presentazioni di nuovi comandi sviluppati dalla comunità di utenti incentrati su:

• nuovi metodi di stima;
• visualizzazione dei dati;
• la connettività di Stata con applicazioni esterne come Python, R o Java;
• automazione avanzata del flusso di lavoro degli utenti;
• strumenti di reporting per la creazione di dashboards o per la pubblicazione automatica su Internet.

Saranno altresì graditi studi applicati in Biostatistica, Istruzione, Epidemiologia, Scienze Sociali e Politiche.

Fedele al formato consolidato negli anni, il secondo giorno della conferenza sarà dedicato alla formazione con il corso “Structural Equation Modelling with Partial Least Squares Using Stata” per ulteriori informazioni sul programma del corso cliccare qui.

COME SOTTOPORRE UN CONTRIBUTO

Gli autori interessati sono invitati a sottoporre un abstract a formazione@tstat.it entro il 10.03.2024. Nell’e-mail dovranno essere inclusi nome, affiliazione, indirizzo e recapito telefonico. Il contributo potrà essere presentato anche in inglese, qualora non fosse possibile farlo in Italiano.

Per ciascuna presentazione saranno disponibili 25 minuti oltre 10 minuti per eventuali domande. Contributi che dovessero richiedere tempi diversi, saranno ben accolti e per la pianificazione del programma sarà necessario segnalarne la durata prevista al momento dell’invio dell’abstract.

Il Comitato Scientifico farà pervenire una risposta entro il 22.03.2024 e la versione definitiva del lavoro dovrà essere inoltrata entro e non oltre il 30.04.2024.

Clicca QUI per scaricare la versione PDF del Call for Papers

Click HERE to download the Call for Papers in PDF format

PROGRAMMA | 9 MAGGIO

8.45 – 9.00 Registrazione dei partecipanti

9.00 – 9.45 SESSIONE I – INVITED SPEAKERS

Structural Equation Modelling with Partial Least Squares using Stata • Sergio Venturini, Università Cattolica del Sacro Cuore Cremona e Mehmet Mehmetoglu, Norwegian University of Science and Technology

Structural equation modeling (SEM) is a multivariate statistical framework that can model both observed and unobserved (latent) variables through complex relationships. In this talk we present plssem, a user-contributed Stata package for partial least-squares SEM, an approach to SEM that has attracted a lot of interest in the last 20 years from an increasing number of researchers and practitioners from many fields such as marketing, information systems, economics, psychology, and others. After introducing the topic to the audience, the talk will illustrate the current architecture of the package and its main features.

9.45 – 11.30 SESSIONE II – COMMUNITY CONTRIBUTED, I

Optimal Policy Learning using Stata • Giovanni Cerulli, IRCrESCNR, Roma

This presentation introduces the Stata package opl for optimal policy learning, facilitating ex-ante policy impact evaluation within the Stata environment. Despite theoretical progress, practical implementations of policy learning algorithms are still poor within popular statistical software. To address this limitation, the package implements three popular policy learning algorithms in Stata (threshold-based, linear-combination, and fixeddepth decision tree), and provides practical demonstrations of them using a real database. Also, I present a policy scenario development proposing a menu strategy, particularly useful when selection variables are affected by welfare monotonicity. Overall, the package contributes to bridging the gap between theoretical advancements and practical applications of policy learning.

Using Marginal Effects for Interpretation in Item Response Theory and in Tests of Differential Item Functioning: Introducing Stata Commands irt_me and irt_dif • Trenton D. Mize, Purdue University, Indiana

The field of categorical data analysis has largely shifted from the limitations of coefficient interpretations to the more flexible and powerful possibilities afforded by marginal effects, spurred by Stata’s widespread implementation of the margins command. Despite item response theory being the latent variable corollary of categorical data analysis, a similar transformation in interpretation tools and practices has yet to emerge. I propose using tests of marginal effects for interpretation in item response theory models, demonstrating the advantages to this strategy over focusing on coefficients. Further, I show how to solve several issues when translating the idea of marginal effects to a latent variable model. A new command, irt_me, automates the estimation of marginal effects after any item response theory model (irt) in Stata, including models with binary, ordinal, nominal, and count items (or a mix).

Differential item functioning is a method of detecting item bias that has traditionally relied on tests of interaction terms in the item response theory coefficients. However, it is well-established in the categorical data analysis realm that coefficients are inappropriate for tests of interaction: tests of the equality of marginal effects are instead the recommended approach. A new command, irt_dif, provides tests of differential item functioning by testing the equality of marginal effects from item response theory models fit across separate groups.

10.45-11.00 Pausa caffè

Too much or too little? New tools for the CCE Estimator • Jan Ditzen, Libera Università di Bolzano, Bolzano

This talk will cover new developments in the literature of the CCE (Common Correlated Effects) and their implementation into Stata. First, I will discuss regularized CCE. CCE is known to be sensitive to the selection of the number of cross-section averages. rCCE overcomes the problem by regularizing the cross-section averages. Secondly, I will discuss the test for the rank condition based on DeVos, Everaert and Sarafidis (2024, Econometrics Reviews). If the rank condition fails, CCE will be inconsistent and therefore testing the condition is key for any empirical application. Finally, I will the selection of crosssection averages using the information criteria from Karabiyik, Urbain, Westerlund (2019, Journal of Applied Econometrics) and Margaritella and Westerlund (2023, The Econometrics Journal).

11.30-12.30 SESSIONE III – EXPLOITING THE POTENTIAL OF STATA 18, I

Causal mediation analysis with Stata • Joerg Luedicke, Senior Social Scientist and Software Developer, StataCorp

Causal inference is an essential goal in many research areas and aims at identifying and quantifying causal effects. By decomposing causal effects into direct and indirect effects, causal mediation provides further insight into underlying mechanisms through which causal effects operate. This talk presents the basic theoretical framework for causal mediation analysis and discusses a variety of examples using Stata’s mediate command. Examples will include linear and generalized linear models using a variety of outcome and mediator variables as well as different types of treatments.

12.30-13.30 Pranzo

13.30-14.30 SESSIONE IV – STATA TIPS AND TRICKS

NNLS: Non-negative least squares using Stata • Giovanni Cerulli, IRCrES-CNR, Roma

The NNLS command enables users to carry out “Non-Negative Least Squares” using Stata calling Python in the background. A simple application of the NNLS Stata command on real data will be provided.

htmltab2stata: Converting html tables into a Stata dataset • Jan Ditzen, Libera Università di Bolzano, Bolzano

htmltab2stata parses html code from websites. It detects tables enclosed with the html <table>environment and transforms the table into a Stata dataset.

Implementing Groupwise – Heteroskedasticity – Robust Variance – Covariance Estimators in Fixed-Effects Panel Data Regression with Stata • Giovanni Bruno, Università Commerciale L. Bocconi, Milano

Stock and Watson (2008) prove that the plain White heteroskedasticity-robust VCE is generally inconsistent for fixed T , N -> ∞ in fixed-effect panel data regression. Bruno (2024) proves that the aforementioned VCE is (fixed T , N _> ∞) consistent under groupwise heteroskedasticity (GH), that is when the conditional variance of the idiosyncratic error is time-invariant, but can vary across individuals. As is well known, the vce(robust) option of xtreg in Stata implements the cluster-robust VCE, not the White VCE. In this paper I show simple Stata procedures to implement the White VCE and a second GH-robust VCE in fixed-effects panel data regression. Monte Carlo experiments prove that both VCEs, under GH, have good finite-sample properties, compared to the bias-adjusted VCE by Stock and Watson and the clusterrobust VCE.

14.30-16.00 SESSIONE V – EXPLOITING THE POTENTIAL OF STATA 18, II

Bayesian model averaging • Meghan Cain, Assistant Director of Educational Services, StataCorp

Are you unsure which predictors to include in your model? Rather than choosing one model, aggregate results across all candidate models to account for model uncertainty with Bayesian model averaging (BMA). Which predictors are important given the observed data? Which models are more plausible? How do predictors relate to each other across different models? BMA can answer these questions and many more.

Stata 18 introduced the bma suite of commands to perform BMA in linear regression models. In this talk, you will learn how to explore influential models, make inferences, and obtain better predictions with BMA. I will demonstrate the utility of BMA for any researcher—Bayesian, frequentist, and everyone in between! No prior knowledge of the Bayesian framework is required.

Text mining in Economics and Health Economics using Stata • Carlo Drago, Università Cusano, Roma

Within the more relevant data science topic, text mining is an important and active research area that offers various ways to extract information and insights from text data. Its continued use and improvement could drive innovation in several areas and improve our ability to interpret, evaluate, and utilize the vast amounts of unstructured text produced in the digital age. Extracting insightful information from text data through text mining in healthcare and business holds great promise. Text mining in business can provide insightful information by analyzing large amounts of text data, including research papers, news, and financial reports. It can help analyze market sentiment, identify emerging trends, and more accurately predict economic indicators by economists. For example, economists can find terms or phrases that reflect investment behavior and sentiment changes by applying text-mining methods to financial news. Text mining can provide essential insights into health economics by examining various textual data, including patient surveys, clinical trials, medical records, and health policy. Researchers and policymakers can use it to understand healthcare utilization patterns better, identify the variables that influence patient outcomes and evaluate the effectiveness of different healthcare treatments. Text mining can examine electronic health data and identify trends in disease incidence, treatment effectiveness and healthcare utilization. In this presentation I will illustrate the instruments currently available in Stata to facilitate a number of text-mining methods.

16.00-16.15 Pausa caffè

16.15-17.30 SESSIONE VI – COMMUNITY CONTRIBUTED, II

geoplot: A new command to draw maps • Ben Jann, Institute of Sociology, University of Bern, Berna

geoplot is a new command for drawing maps from shape files and other datasets. Multiple layers of elements such as regions, borders, lakes, roads, labels, and symbols can be freely combined and the look of elements (e.g. color) can be varied depending on the values of variables. Compared to previous solutions in Stata, geoplot provides more user convenience, more functionality, and more flexibility. In this talk I will introduce the basic components of the command and illustrate its use with examples.

Fitting spatial autoregressive logit and probit models using Stata: The spatbinary command • Daniele Spinelli, Università degli Studi di Milano-Bicocca, Milano

Spatial regressions can be estimated in Stata using the spregress, spxtregress, and spivregress commands. These commands allow users to fit spatial autoregressive models in cross-sectional and panel data. They are designed to estimate regressions with continuous dependent variables. The spatbinary command now allows Stata users to fit spatial logit and probit models, important models in applied econometrics.

17.30-17.50 SESSIONE VII – STUDI APPLICATI CON STATA

Do Alternative Work Arrangements Substitute Standard Employment? Evidence from Worker-Level Data • Filippo Passerini, University of Bologna e LABORatorio R. Revelli, Bologna

This study analyses the impact of an Alternative Work Arrangement (AWA) called “voucher” on earnings of atypical workers and on their alternative income sources using Italian administrative data. Specifically, we investigate whether this form of very flexible work substitutes income from more standard labor contracts and welfare transfers related to employment insurance (sick and parental leave and unemployment benefits). We estimate cross-income elasticities using fixed effects and diff-in-diff specifications that correct for sample selection of individuals in the labor market. Results show that vouchers increase overall labor income, but they also substitute earnings derived from other labor contracts. We do not find relevant associations between vouchers and welfare transfers. The positive effect of vouchers on total income is smaller in specifications that correct for sample selection bias, and the substitution effect with other labor income sources is substantially larger. Overall, our findings show that AWAs tend to substitute standard employment, with small positive net effects on earnings, which are larger for intensive users of vouchers, and in geographic regions with a more sizable informal sector.

17.50-18.15 OPEN PANEL DISCUSSION WITH STATA DEVELOPERS • JOERG LUEDICKE AND MEGHAN CAIN, STATACORP

La sessione “Open panel discussion with Stata Developers” offre ai partecipanti la possibilità di interagire direttamente con la StataCorp: sarà possibile evidenziare problemi o limitazioni del software nonché suggerire eventuali miglioramenti o comandi che potrebbero essere inclusi in Stata.

20.00 Cena Sociale (opzionale)

Clicca qui per scaricare la versione PDF del programma in Italiano.

Click here to download the program in PDF format.

10 Maggio 2024

Corso di formazione “Introduction to Partial Least Squares Structural Equation Modelling (Pls-Sem) Using Stata” • Sergio Venturini, Università Cattolica del Sacro Cuore Cremona e Mehmet Mehmetoglu, Norwegian University of Science and Technology

La conferenza si terrà il 9-10 Maggio 2024 a Firenze, presso presso Villa la Stella, Via Jacopone da Todi, 12.

I prezzi si intendono IVA 22% esclusa. L’aliquota IVA non sarà applicata per Enti Pubblici soggetti ad esenzione a norma dell’art. 14 c. 10 della L. 537/93 per la partecipazione a corsi di formazione dei propri dipendenti.

La quota di iscrizione include: i) il materiale didattico; ii) licenza temporanea del software Stata 18; iii) pause caffè; iv) pranzo; e, per coloro che sceglieranno il pacchetto completo, v) pernottamento in camera singola con trattamento bed and breakfast, di una notte per iscritti al pacchetto 1 (ingresso 08/05) e due notti per iscritti al pacchetto 2 (ingresso 08/05) presso Villa la Stella.

TERMINE DI ISCRIZIONE: La domanda di partecipazione dovrà avvenire tramite il modulo di registrazione, disponibile presso la segreteria organizzativa, entro il 30.04.2024.

SPONSORSHIP PER DOTTORANDI

TStat è lieta di offrire gratuitamente a 2 dottorandi full-time il “Pacchetto 2” (i costi relativi al trasferimento e quanto non espresso nella sessione “la quota di iscrizione include” sono esclusi) grazie al progetto “Investire nei Giovani Oggi” con il quale, ogni anno, si pone l’obiettivo di sostenere il percorso formativo di giovani ricercatori nei paesi dove è distributrice del software Stata. L’ammissione avverrà a seguito dell’invio del proprio curriculum vitae e successiva valutazione in base al percorso di studio, nonché all’ordine di arrivo delle richieste.