Template-Type: ReDIF-Paper 1.0
Testing a (not-quite) necessary condition for parallel trends in difference in differences using a CCC test
Author-Name: Sunny Karim
Author-Workplace-Name: Carleton University
Creation-Date: 20261107
Abstract:  In the difference-in-differences literature, the average treatment effect on the treated (ATT) is typically considered identified under the conditional parallel trends (CPT) assumption, often without recognizing the overly restrictive nature of the covariate structure it imposes. In practice, conventional methods for assessing the CPT's plausibility rely on the assumption that covariates affect untreated potential outcomes through common coefficients across regions and periods, known as the common causality of covariates assumption (Karim and Webb 2024). Within this conventional framework, Karim and Webb (2024) demonstrate that CCC is a necessary condition for CPT. Intersection parallel trends (IPT) extend this framework by allowing more flexible incorporation of covariates, potentially revealing plausible parallel trends that traditional methods might miss. The CCC is no longer a necessary condition for intersection parallel trends (IPT), because it allows covariate effects to vary. This presentation introduces a formal statistical test for the CCC assumption, checking whether covariate coefficients are stable across regions, periods, or both. The CCC test employs a sequential F test to examine homogeneity, region-specific variation, time-specific variation, or combined variation. In the first stage, it assesses the equality of covariate coefficients across all untreated region-period cells. If rejected, the second-stage tests evaluate region and time variation separately, using Holm’s step-down procedure to control the family-wise error rate. This decision rule helps researchers choose the appropriate DID-INT model. We also provide a Stata program that automates CCC testing and suggests the best DID-INT specification. Although direct testing of parallel trends is not statistically testable, the CCC test offers guidance on selecting a specification likely to uphold intersection parallel trends, thereby yielding consistent ATT estimates.
Handle: RePEc:boc:csug26:01

Template-Type: ReDIF-Paper 1.0
threepm: estimating lifetime and episode-of-illness costs under censoring
Author-Name: Robert McGowan
Author-Workplace-Name: University of Colorado
Creation-Date: 20261107
Abstract:  Researchers routinely estimate costs over a lifetime or an episode, for example, expenditures after an illness. Cost data are often right-censored because of death or end of either follow-up or episode. Basu and Manning (2010) introduced an estimator that extended the existing class of two-part models to accommodate noninformative right-censoring with continuous death and censoring times. Their three-part estimator combines a parametric survival model with person-interval two-part cost models. The estimator is stable when censoring is heavy and costs are skewed with many zeros. Inverse-probability weighting methods (Bang and Tsiatis 2000; Lin 2000) become unstable in these conditions. The estimator also decomposes covariate effects on cumulative cost into effects on survival and effects on the intensity of utilization. I introduce threepm, a Stata command implementing the Basu and Manning estimator. threepm reports cumulative marginal effects and cost and decomposes between-group differences into a rate of accumulation component and a survival component. It accommodates equal or unequal interval lengths, different rates of cost accumulation during the end-of-life window, a choice of survival distributions and of cost-model families and links, and person-clustered bootstrap inference, with companion commands for specification tests, marginal effects, and plotting.
Handle: RePEc:boc:csug26:02

Template-Type: ReDIF-Paper 1.0
Parenting and child behavioral problems: Old questions and new panel-data analyses
Author-Name: Andrew Grogan-Kaylor
Author-Workplace-Name: University of Michigan
Creation-Date: 20261107
Abstract:  Prior research has documented associations between parenting, family, and neighborhood contexts and child behavior problems, but many findings rely on analytic approaches that are limited in their ability to account for stable family and background characteristics. Thus, the causal strength of these associations remains uncertain. This presentation examines the extent to which parenting, family, and neighborhood factors were associated with child externalizing and internalizing behavior problems using methods designed to strengthen causal inference. Using longitudinal panel data from N = 2,701 families in the future of families and child well-being study, I estimated fixed-effects, random-effects, and correlated random-effects models allowing us to compare between-family and within-family estimates while accounting for observed and unobserved time-invariant heterogeneity. Some of these models have not been previously used in analyses of parenting and child development. Fixed-effects and random-effects models are long-standing in Stata, but correlated random effects are newer to Stata. Similarities across these models are discussed. Across models, parental spanking was consistently associated with higher levels of externalizing and internalizing behavior problems, whereas increases in caregiver warmth were associated with lower internalizing behavior problems. Findings support interventions and policies aimed at reducing physical punishment and increasing parental warmth.
Handle: RePEc:boc:csug26:03

Template-Type: ReDIF-Paper 1.0
Antenatal care visits and early neonatal mortality among pregnant women transported by emergency medical services—cohort data analysis
Author-Name: Khaleda Adib Binte Abdullah
Author-Workplace-Name: Harvard Medical School
Creation-Date: 20261107
Abstract:  In India, there are many low-resource areas where pregnant women often have ≤3 ANC visits, which can lead to missed complications. Emergency medical services (EMS) transport programs were launched to help pregnant women reach healthcare facilities quickly. Research showed that these programs have not significantly reduced early neonatal deaths. To see how the number of ANC visits relates to early neonatal mortality, a secondary analysis was performed. Methods: The study included 1,431 live births from February to April 2014 among mothers who used EMS transport. Stillbirths and incomplete records were excluded during data management. Mothers' ANC visits, divided into two groups as ≤3 and ≥4, align with the minimum requirement of the WHO guidelines. Stata v19 BE is used for statistical analysis with multivariable logistic regression. The adjusted odds ratios had no significant changes in neonatal mortality on delivery method, place of delivery, or region. Results: Among mothers with minimal ANC (1–3 visits), early neonatal mortality was 7.9% (89 out of 1,128), whereas it was 1.6% (5 out of 303) among those with 4 or more visits. The unadjusted odds ratio was 5.3 (95% CI 2.1–13.2). Even after controlling for other factors, not receiving the recommended amount of antenatal care (ANC) remained associated with a higher risk.
Handle: RePEc:boc:csug26:04

Template-Type: ReDIF-Paper 1.0
Financial statistics in Stata
Author-Name: David Schenck, 
Author-Workplace-Name: StataCorp
Creation-Date: 20261107
Abstract:  This talk introduces a suite of new commands in Stata for financial statistics. These new commands are finreturns for easily building various types of asset returns from prices; finportfolio for constructing portfolios; finsummarize for computing summary statistics; finregress for running capital asset pricing model and Fama–MacBeth regressions; and finvalrisk for evaluating historical and model-based value at risk. I combine these commands with Stata's extensive time-series toolkit to demonstrate a workflow for analyzing financial data.
Handle: RePEc:boc:csug26:05

Template-Type: ReDIF-Paper 1.0
Title: Energy efficient or carbon efficient? Examining building performance in British Columbia using Stata
Author-Name: Harmeet Sond
Author-Workplace-Name: University of British Columbia
Creation-Date: 20261107
Abstract: We often assume if a building uses energy efficiently, it must be better for the environment, but the relationship between energy use and carbon performance is not always clear. This matters when cities are deciding where to invest, because relying on energy metrics alone could lead to substantial capital investments without achieving the expected carbon reductions. That brings up key questions: To what extent do energy use intensity (EUI) and greenhouse gas intensity (GHGI) tell the same story about building performance, and what building characteristics help explain differences between the two? Using public data from Building Benchmark BC, I will examine factors such as building age, property type, floor area, location, and heating fuel sources. Stata will be used to organize the data, visualize patterns, estimate regression models for EUI and GHGI, and run adjusted predictions to compare how building characteristics are associated with each metric. These analyses will highlight where energy and carbon performance may align, where they diverge, and which factors may explain those differences. Ultimately, the project demonstrates how Stata turns real building data into clear evidence to help policymakers and owners target the right buildings, refine decarbonization priorities, and make smarter climate choices in British Columbia.
Handle: RePEc:boc:csug26:06

Template-Type: ReDIF-Paper 1.0
Title: Impact of physical infrastructure on children education in Pakistan
Author-Name: Urooj Chandani
Author-Workplace-Name: University of California San Diego
Creation-Date: 20261107
As the global education coalition (GEC) marks its fifth anniversary, UNESCO, in collaboration with the United Nations Department of Economics and Social Affairs, faces the context of 251 million children and youth still out of school worldwide (UNESCO 2025). With an estimated out-of-school population of 23 million, Pakistan has the second-highest number of out-of-school children between the ages of 5 and 16 in the world (Ansari 2021). To address this problem, this study attempts to investigate the effect of school infrastructure on the educational status and learning levels for children in K–12 settings in Pakistan for the years 2019, 2021, and 2023. It utilizes the raw dataset made publicly available by the annual status of educational report (ASER) Pakistan—the largest citizen-led national household-based initiative.
Given this background, this presentation will try to address the following two research questions:
What is the impact of school infrastructure characteristics on the educational status of the child (3–16 years) in Pakistan for the five-year period 2019–2023, after controlling for student characteristics of the child and parental education?
What is the impact of school infrastructure characteristics on the basic learning levels of the child (3–16 years) in Pakistan for the five-year period 2019–2023, after controlling for student characteristics of the child and parental education?
The study utilizes ordinary least-squares and ordered probit regressions for analyzing the conditional correlation for the above two questions across 161 districts of Pakistan, the smallest functional unit of local governance. For the outcome variable educational status of the child, the predictor variables for school characteristics of boundary wall, bathroom, and school furniture are statistically significant. For parental education, both father's and mother's education are statistically significant. Student characteristics of age, gender and overall interaction are also statistically significant. For the outcome variable basic learning levels, reading in local and national language, the predictor variables for school characteristics of furniture and internet are statistically significant. For parental education, both father's and mother's education are statistically significant. Student characteristics of age and the interaction between age and gender are also statistically significant.
Handle: RePEc:boc:csug26:07

Template-Type: ReDIF-Paper 1.0
Title: District-level changes in overweight and obesity among women in Punjab: Evidence from the National Family Health Survey, 2015–2021
Author-Name: Amandeep Singh
Author-Workplace-Name: Queen's University
Creation-Date: 20261107
Abstract: Overweight and obesity among women have increased substantially in India over the past decade and contribute to the growing burden of noncommunicable diseases. Punjab consistently reports one of the highest prevalences in the country, yet district-level changes over time remain poorly described. This study examines geographic changes in overweight and obesity among women aged 15–49 years across Punjab using two rounds of the National Family Health Survey (NFHS). Methods: A comparative cross-sectional analysis will be conducted using NFHS-4 (2015–16) and NFHS-5 (2019–21). Women aged 15–49 years residing in Punjab will be included. Overweight and obesity will be defined as body mass index (BMI) ≥25 kg/m2. Survey-weighted analyses accounting for the complex sampling design will be performed in Stata 19 to estimate district-level prevalence, quantify percentage-point changes between survey rounds, and examine geographic variation across districts. Preliminary results: Preliminary analyses indicate that the prevalence of overweight and obesity among women increased between NFHS-4 and NFHS-5, with marked heterogeneity across Punjab. Several districts experienced substantially larger increases than others, suggesting that the burden of overweight and obesity is not uniformly distributed across the state. Conclusion: Identifying districts with the greatest increases in overweight and obesity can help prioritize local public health interventions and inform future investigations of socioeconomic and environmental determinants. This study demonstrates the application of Stata for analysing complex survey data from a nationally representative health survey.
Handle: RePEc:boc:csug26:08

Template-Type: ReDIF-Paper 1.0
Title: Severe food insecurity is associated with obesity among Mexican school-aged children: ENSANUT continua 2021–2023
Author-Name: Ana Maria Maldonado Garza
Author-Workplace-Name: McGill University
Creation-Date: 20261107
Abstract: Mexico continues to face a crisis of food insecurity (FI) and obesity (OB). Nearly 45% of Mexican households lived with FI. Simultaneously, the country holds one of the highest rates of overweight and obesity (OWOB) worldwide, with alarming trends among youth. While a link between FI and OWOB is well established in adults, evidence in children remains inconclusive. FI and OWOB impair both physical and cognitive development, increasing long-term health risks. Objectives: 1. Evaluate the association between FI and body mass index z-score (z-BMI) by assessing if the mean BMI significantly varies across FI severity levels. 2. Assess the likelihood of OWOB, OW, and OB in children across FI severity levels. Methodology: Secondary analysis from ENSANUT 2021–2023, which holds a national, probabilistic, stratified, and clustered design, was used to examine the association between FI and OWOB in children (5–11y). FI was collected using the Latin-American and Caribbean food security scale (ELCSA), and OWOB was derived from z-BMI scores calculated from anthropometric data. Analysis of variance was used to assess differences in mean z-BMI across FI severity levels, while logistic regression models estimated odds ratios (ORs) for OWOB outcomes according to FI severity level. Results: Data from 3,953 children (representing 15.5 million) were analyzed. 62.4% were FI (26.7% moderate to severe), and 36.4% were OWOB. No significant association was observed between FI severity and mean z-BMI. In adjusted logistic regression models, severe FI was associated with higher odds of OB (OR=1.76, 95% CI: 1.03-2.99, p=0.037) and compared with males, females had lower odds of OB (OR=0.51, 95% CI: 0.39-0.68, p **(presenter hit word limit and p-value was cut off)**
Handle: RePEc:boc:csug26:09

Template-Type: ReDIF-Paper 1.0
Title: PCA-based estimation in two-phase sampling: A Stata implementation and simulation study
Author-Name: Amber Asghar
Author-Workplace-Name: Virtual University of Pakistan
Creation-Date: 20261107
Abstract: Estimation of population parameters using two-phase sampling designs offers an efficient platform in the presence of auxiliary information; however, multiple correlated auxiliary variables may complicate the estimation process. In this presentation, a principal component analysis (PCA)-based method is proposed to incorporate multiple correlated auxiliary variables in estimation using two-phase sampling designs. This approach helps to reduce the number of correlated auxiliary variables while retaining the relevant principal components. A full implementation of the proposed estimator is provided using Stata software, which consists of data preparation, principal component analysis, computation of the estimator, and evaluation of the sampling performance. A simulation study is performed to evaluate the performance of the proposed estimator in terms of bias, mean squared error, and relative efficiency. The proposed estimator is also evaluated in comparison with some existing estimators. The current study provides the reproducible Stata code to implement the proposed PCA-based estimation using two-phase sampling designs without writing the matrix program codes.
Handle: RePEc:boc:csug26:10

Template-Type: ReDIF-Paper 1.0
Title: Building household wealth indices in Stata: A reproducible workflow for measuring health inequalities in low-resource settings case study: Cervical cancer diagnosis in Mozambique
Author-Name: Francisco Azevedo-Fernandes
Author-Workplace-Name: Technical University of Mozambique
Creation-Date: 20261107
Abstract: Measuring socioeconomic position remains a major challenge in epidemiologic research conducted in low-resource settings, where informal employment, remittances, seasonal income, cash-based economies, and limited financial reporting reduce the reliability of reported household income. Asset-based household wealth indices provide a more stable measure of long-term socioeconomic position and are widely used to investigate health inequalities when direct income measures are unavailable. This presentation demonstrates a reproducible Stata workflow for constructing and applying an asset-based household wealth index using cervical cancer diagnosis at the oncology department of Maputo Central Hospital, Mozambique's largest national referral hospital and a major oncology referral center in Sub-Saharan Africa, as a case study. The workflow includes data cleaning and recoding; construction of binary household asset variables; principal component analysis (PCA); assessment of sampling adequacy using the KMO statistic and Bartlett's test; generation of standardized wealth scores and wealth quintiles; and multivariable logistic regression with robust standard errors to examine associations with late-stage diagnosis. Practical approaches for handling missing data, producing publication-ready tables and figures, documenting reproducible analytical workflows, and interpreting findings for epidemiologic and health equity research are also presented. Moving beyond disease-specific findings, this presentation showcases a complete, reproducible Stata workflow, from data preparation and wealth index construction to multivariable modeling and publication-ready outputs. The methods are readily transferable to studies of health inequalities across diverse diseases and low-resource settings, providing researchers with practical tools for generating robust, transparent, reproducible, and policy-relevant evidence.
Handle: RePEc:boc:csug26:11

Template-Type: ReDIF-Paper 1.0
Title: Merger simulation without price data: npmergersim
Author-Name: Victor Aguirregabaria
Author-Workplace-Name: University of Toronto
Creation-Date: 20261107
Abstract: This presentation introduces npmergersim, a community-contributed command for merger simulation in differentiated-product markets when price data are unavailable, confidential, or highly aggregated. The command implements the method developed in Aguirregabiria, Clark, and Souza-Rodrigues (2026), which substitutes Bertrand pricing equations into the demand system to obtain a set of equilibrium conditions in which market shares are the only endogenous variables. This eliminates the need for price data to identify demand substitution patterns, price-cost margins, profits, consumer surplus, and merger effects. npmergersim builds directly on the mergersim command of Björnerstedt and Verboven (2014), extending it along several dimensions: estimation and counterfactual simulation using market shares alone; alternative models of firm conduct, including collusive and conduct-parameter specifications; and quantity competition as an alternative to Bertrand pricing. The command also provides a built-in specification test that compares estimates obtained with and without price data, allowing researchers to assess whether observed prices are contaminated by economically meaningful measurement error. I illustrate the command using data from the European automobile industry, replicating the merger simulation in Björnerstedt and Verboven (2014) without relying on price information.
Handle: RePEc:boc:csug26:12

Template-Type: ReDIF-Paper 1.0
Title: How market concentration and counterfactual consolidation through chain mergers and acquisitions affect pricing, quality provision, and firm markups: Evidence from the nursing home industry in Pennsylvania
Author-Name: Abdullah Alolayan
Author-Workplace-Name: Concordia University
Creation-Date: 20261107
Abstract: This presentation examines how market concentration affected market outcomes (prices, quality, and supply) in Pennsylvania nursing-home markets from 1997 to 2014, with particular attention to differences between nonprofit and for-profit facilities and between independent and chain-affiliated nursing homes. Using facility-level panel data, instrumental-variable methods, fixed effects, dynamic system GMM, and structural discrete-choice demand models (standard logit, standard random coefficients, and full random coefficients BLP), I find that greater market concentration is consistently associated with higher private-room prices, while the evidence regarding skilled-nurse staffing is mixed and generally statistically insignificant; higher concentration is also associated with reductions in supply. These findings remain robust when concentration is measured using a normalized Herfindahl–Hirschman index and when markets are defined by zip code rather than county, with endogeneity addressed using BLP-style leave-one-out instruments based on rival and other-market characteristics. The structural estimates indicate that private-payer demand is downward sloping, with average own-price elasticities ranging from –3.187 to –5.903 across the random-coefficient specifications. Nonprofit nursing homes face somewhat more elastic demand and exhibit higher recovered marginal costs than for-profit facilities, while their prevalence declines substantially over the sample period. Counterfactual merger simulations show that the consolidation of the two largest chains, together with the acquisition of two independent facilities in each affected market, raises average daily private-room prices by approximately $6.64 to $10.18, depending on the demand specification, whereas alternative consolidation scenarios generate smaller price increases. Conversely, dissolving chain ownership produces meaningful reductions in private room prices. The welfare analysis indicates that merger-induced price increases impose material losses on private-paying residents and that the estimated harm is greater under the richer random-coefficient specification, suggesting that more restrictive demand models may understate consumers’ vulnerability to market consolidation.
Handle: RePEc:boc:csug26:13

Template-Type: ReDIF-Paper 1.0
Title: Estimating competition in microfinance using Stata: A stochastic frontier and dynamic panel GMM approach
Author-Name: Pooja Khari
Author-Workplace-Name: Indian Institute of Foreign Trade
Creation-Date: 20261107
Abstract: Estimating competition using the Boone index requires the integration of multiple econometric techniques, including stochastic frontier analysis, translog cost modeling, and dynamic panel estimation. This presentation demonstrates a reproducible Stata workflow for measuring competition in the global microfinance sector using panel data from the microfinance information exchange (MIX) and the World Bank's world development indicators for the period 2010–2019. The analysis illustrates how Stata can be used to estimate a translog cost function through stochastic frontier analysis, derive institution-level marginal costs, and subsequently estimate Boone coefficients using a two-step generalized method of moments (GMM) framework. The presentation further demonstrates the use of Stata for panel-data management, model estimation, diagnostic testing, and graphical visualization of competition dynamics across countries and over time. As an empirical application, the proposed workflow is used to examine competition across microfinance institutions operating in developing economies. The results reveal predominantly negative Boone coefficients for prolonged periods in most upper and lower developing countries, indicating sustained competitive pressures, while several lower and least developed economies exhibit weaker competition as reflected by positive Boone coefficients. India demonstrates consistently strong competition throughout the study period relative to its peer economies, although an anomalous positive Boone coefficient in 2016 suggests a temporary decline in competitive intensity. Graphical analyses generated in Stata further illustrate the evolution of competition across different country groups, highlighting heterogeneous competitive dynamics associated with varying levels of economic development. Beyond its empirical findings, the presentation provides Stata users with a practical framework for implementing advanced competition analysis by integrating stochastic frontier estimation, translog cost modeling, dynamic panel GMM estimation, and reproducible visualization techniques within a single analytical workflow. The methodology is readily adaptable to other industries and countries where competition measurement using firm-level panel data is of interest.
Handle: RePEc:boc:csug26:14

Template-Type: ReDIF-Paper 1.0
Title: Monetary policy and business cycle fluctuations in emerging and developing countries
Author-Name: Chop Kanta Subedi
Author-Workplace-Name: Nepal Rastra Bank
Creation-Date: 20261107
Abstract: This presentation examines how monetary policy and global uncertainty impacted business cycle fluctuations in emerging and developing economies over 1991–2025. Business cycle dynamics are captured using the cyclical components of real GDP, consumption, investment, and net exports, while global uncertainty is proxied by the world uncertainty index. I document that output is more volatile than growth and highly persistent, indicating the predominance of persistent shocks, whereas output growth is relatively stable and transitory. Consumption exceeds output volatility and closely comoves with it, suggesting weak consumption smoothing. Investment is strongly procyclical and highly volatile, amplifying fluctuations, and net exports are markedly countercyclical. Using the local projection method, I find that contractionary monetary policy shocks induce statistically significant and persistent declines in output, consumption, and investment, with only weak and short-lived effects on net exports. Monetary transmission is attenuated, reflecting structural rigidities and shallow financial systems. In addition, global uncertainty shocks exert contractionary effects on domestic demand and amplify business cycle volatility, with heterogeneous responses across countries. These findings highlight the limited effectiveness of conventional monetary policy under structural constraints and underscore the need to strengthen financial development and transmission mechanisms to enhance macroeconomic stabilization.
Handle: RePEc:boc:csug26:15

Template-Type: ReDIF-Paper 1.0
Title: Advancing statistical practice with Stata: Time-series modeling for tourism demand analysis
Author-Name: Dila Bhandari
Author-Workplace-Name: Tribhuvan University
Creation-Date: 20261107
Abstract: The increasing complexity of modern datasets has heightened the demand for robust, reproducible, and efficient statistical methodologies. This presentation explores the application of Stata in addressing contemporary challenges in data management, statistical modeling, visualization, and reproducible research across diverse disciplines. I demonstrate the application of Stata for analyzing and forecasting tourism demand using modern time-series techniques. The analysis employs an ARIMA model to capture historical trends, seasonality, and temporal dependence in tourist arrivals. The modeling process includes data visualization, stationarity testing using the ADF test, model identification through ACF and PACF plots, parameter estimation, diagnostic checking, and out-of-sample forecasting. Model performance is evaluated using forecasting accuracy measures such as RMSE and MAPE. The study highlights Stata's capabilities for reproducible time-series analysis and demonstrates how ARIMA-based forecasting can provide reliable evidence to support tourism planning and decision-making under changing economic and seasonal conditions. The model ARIMA (p,d,q), where ϕ(B)(1−B)dYt=θ(B)εt, Yt represents tourist arrivals at time t, B is the backshift operator, ϕ(B) is the autoregressive polynomial, θ(B) is the moving average polynomial, and εt denotes white-noise errors.
Handle: RePEc:boc:csug26:16