The 2019 Canadian Stata Conference was held on 30 May at the Banff Centre for Arts and Creativity.
Session I: Causal inference
Investigating exporting under credit constraints using marginal treatment effects estimation
Abstract: This presentation explores the impact financial considerations have on firm export, investment behaviour, and productivity growth using marginal treatment effects estimation. The research has two goals. First, we attempt to quantify the positive selection on unobserved heterogeneity in a firm's return to exporting. This return may potentially reflect differences in firms' access to credit markets. The first goal is to update the learning-by-exporting literature to account for ex-ante differences in financial health and credit constraints. Quantifying this difference allows for improved understanding of the interaction between trade, financial markets, and productivity growth. Second, this presentation also provides a general framework under which we consider the minimal conditions required for the identification of "constrained" agents (firms) relative to their unconstrained counterparts. The researcher observes only an agent's actual action, but not any particular agent's constraints. We are likely to confound estimates because we are unable to distinguish the reason an agent does not engage in a behaviour, in this case, a firm not exporting or investing. There is potential to confuse firms who expect low returns from an investment and thus do not invest with those who could not invest because of credit constraints. To overcome this identification problem for this set of heterogeneous firms, we perform a marginal treatment effects estimation. Specifically, we use the Stata module mtefe (Andresen 2018) to assess exporting and a firm's productivity growth while accounting for the potential of credit constraints.
Inference after lasso model selection
Abstract: The increasing availability of high-dimensional data and increasing interest in more realistic functional forms have sparked a renewed interest in automated methods for selecting the covariates to include in a model. I discuss the promises and perils of model selection and pay special attention to some new estimators that provide reliable inference after model selection.
Session II: Household finance
Removing the fine print: Standardization, disclosure, and consumer outcomes
Abstract: Consumers face a choice when evaluating financial contracts: study the fine print and incur a cognitive cost, or ignore it and risk costly surprises in the future. We use a pair of policy changes in Chile to contrast two measures to protect consumers from fine print; the first improves disclosure, and the second standardizes and regulates contract features. With administrative data from the banking regulator on consumer loans, we use a regression discontinuity design to estimate the causal effects of these regimes. Consumers offered standardized contracts experienced 40% (14.4 percentage points) less delinquency. Using a difference-in-differences design, we find that sophisticated borrowers are helped most by increased disclosure, while unsophisticated borrowers benefit more from product standardization. Additionally, we show that only sophisticated borrowers who benefit from the informational disclosure treatment leave less "money on the table." We contextualize these results in a stylized model that predicts that financially sophisticated borrowers will benefit from disclosure, while unsophisticated borrowers will benefit from standardization based on differentials in the cost of studying.
University of Virginia
Home equity line of credit utilization and arrears
Abstract: Using Canadian consumer microcredit data from 2010–2018, I analyze the relationships between consumers' home equity line of credit (heloc) utilization, macroeconomic factors, and consumer-specific characteristics. Major shares of heloc loans are held by borrowers with high credit quality at origination. Our estimates show that a 10 percent increase in local house price leads to about a $1,600 increase in outstanding heloc loan amount. However, the heloc loans as a ratio of local house prices do not respond to house prices in terms of economic significance, implying that consumers may have targeted their loans to certain leverage ratio. Neither the heloc interest rate nor the provincial unemployment rate has an economic significant relationship with heloc outstanding loan amounts. Regarding heloc delinquency, we found that a majority of accounts in arrears exhibit over 80 percent utilization. Delinquency is mainly driven by consumer-specific factors, and macroeconomic factors except house price changes have only minor influences.
Bank of Canada
Losing contact: The impact of contactless payments on cash, debit, and credit card usage
Abstract: In this presentation, I am investigating the following "puzzle": in aggregate share trends, cash seems displaced by contactless credit card (CTC) payments; however, at the micro level, regression analysis finds no significant effect of CTC on cash once unobserved heterogeneity is accounted for. I relax the assumption of homogeneous coefficients across all individuals and evidence the existence of different micro substitution effects that are confounded in aggregate data. All the data analysis is conducted in Stata using, i.a., panel data analysis, cluster analysis, extended linear regression and finite mixture models.
Bank of Canada
Nonlinear dynamic stochastic general equilibrium models
Abstract: Dynamic stochastic general equilibrium (DSGE) models are used in macroeconomics for policy analysis and forecasting. A DSGE model is a system of
Session III: Survival models
ipdmidas: Meta-analytical integration of individual participant diagnostic test data
Abstract: This presentation will discuss a new community-contributed command, ipdmidas, that facilitates implementation of models for meta-analysis of individual patient data (IPD) from both a frequentist and Bayesian perspective. Meta-analysis of diagnostic test studies typically involves synthesizing aggregate data (AD)Ben, such as the 2 x 2 tables of diagnostic accuracy. Bivariate random-effects meta-analysis (BREM) and the hierarchical summary ROC (HSROC) model can appropriately synthesize these tables, leading to clinical results such as the summary sensitivity and specificity and summary ROC(SROC) curves across studies. However, translating such results into practice is often limited by between-study heterogeneity and application to some "average" patient across studies. Meta-analysis of IPD examines study-level covariates, explains the between-study heterogeneity, and examines patient-level covariates, assessing the effect of patient characteristics on test accuracy. This allows tailoring of test results to the individual patient and informs individual diagnostic strategies. I will show how BRMA/HSROC models have been and maybe extended to IPD to depict how covariates affect sensitivity, specificity, and between-study heterogeneity and correlation. I will also demonstrate how ipdmidas can be used to obtain such metrics and other informative results such as the diagnostic odds ratio, the positive and negative likelihood ratio tests, and the SROC curve.
Ben Adarkwa Dwamena
University of Michigan Medical School
The minimum wage, turnover, and the shape of the wage distribution
Abstract: This presentation proposes an empirical approach for jointly modeling the impact of the minimum wage on the wage distribution and on movements in and out of the workforce. We estimate the effects of the minimum wage on the hazard rate for wages, which provides a convenient way of rescaling the wage distribution in the presence of employment effects linked to the minimum wage. We use the estimates to decompose the distributional effects of minimum wages into effects for workers moving out of employment, workers moving into employment, and workers continuing in employment. The estimator is implemented in Stata using the command for generalized linear models.
University of Winnipeg
Innovation, survival, and growth: Evidence from a cohort of US startups
Paper abstract: The launch of new business ventures is an important source of dynamism for both advanced and transitioning economies. However, survival prospects are low and many new business ventures remain small. Yet, the empirical evidence from administrative level data suggests that much of aggregate employment and productivity gains stem from a small subset of successful,including high-growth, startups; however, these data often lack information on firm strategy,financing, innovation activities and founder characteristics, among other variables. Using a novel detailed survey dataset, the Kauffman Firm Survey, we study a representative cohort of American startup firms launched in 2004 over an eight-year period until 2011; overlapping with the business cycle pre and post the Great Recession of 2008-2009. Considering a rich set of firm-level factors including financing conditions, we examine the role of innovation measured by the firms industrial technology sector as well as self-reported innovation and R&D activities in driving firm survival and performance. We also investigate the role of innovation in securing external financing as a potential mechanism in early stage firm growth.
Presentation abstract: This presentation proposes an empirical approach for jointly modeling the impact of minimum wage on the wage distribution and on movements in and out of the workforce. We estimate the effects of the minimum wage on the hazard rate for wages, which provides a convenient way of rescaling the wage distribution in the presence of employment effects linked to the minimum wage. We use the estimates to decompose the distributional effects of minimum wages into effects for workers moving out of employment, workers moving into employment, and workers continuing in employment. The estimator is implemented in Stata using the command for generalized linear models.
Universite d'Auvergne Clermont-Ferrand I
Session IV: Pedagogy
Improving statistical learning using videos to complement lectures
Abstract: Can instructional videos help improve learning in undergraduate courses in business statistics? With the proliferation of online learning materials available as MOOCs, institutions of higher learning are also actively experimenting with innovative models of blended learning. Online content presents an interesting opportunity for instructors to complement their face-to-face interactions with students with online videos and other digital resources. I present the results of an experiment with a large number of students (n = 1202) who were enrolled in the second course in business statistics in a business faculty at a North American University. The students, without their knowledge, were randomly divided into control and treated groups. Students in the treated group were encouraged (not required) to watch brief videos that reinforced concepts already introduced in the lecture hall. Students in the control group were not advised as such. The treated group was monitored online to measure individual students' level of interaction with the online content (especially videos). Afterwards, students' performance in assignments and exams was compared between the treated and control groups while controlling for the level of engagement with the online materials. Furthermore, students' responses to questions with the highest correct and wrong responses were analyzed. This presentation describes the findings from the experiment and shares insights for reinforced learning using online videos to improve learning and pedagogy in undergraduate courses in business statistics.
Open panel discussion with Stata developers
Presentations from StataCorp
Matthew Webb (Chair)
Bank of Canada
University of Waterloo
Calgary Statistical Support
Presenters from StataCorp
David M. Drukker is the Executive Director of Econometrics at Stata. His passion for programming enabled him to start working at Stata in 2000, when he finished his Ph.D. in economics at the University of Texas at Austin. Since then he has developed many Stata commands for estimating treatment effects and for analyzing panel data, time-series data, and cross-sectional data. He played a key role in the initial development of Stata MP, helped integrate Mata into Stata, and helped develop Stata and numerical techniques.
David Schenck is a Senior Econometrician at StataCorp and is the primary developer of Stata's new DSGE features. He has a bachelor's degree in economics from Vanderbilt University and a master's degree in economics from Boston College. His research interests lie in macroeconometrics.
Registration and accommodations
Registration is closed.
|Users dinner (optional)||$45.00 USD|
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