Many improvements to inference have been suggested, including bootstrap and jackknife methods, in addition to alternative standard errors and degrees of freedom. This presentation will discuss how to use jackknife methods in Stata for improved inference. We detail the new Stata ado-command, summclust, which offers both improved inferences and diagnostic tools for assessing when conventional errors can be problematic. We also discuss jackknife methods for two different situations: linear models with multiway clustering and nonlinear models with one-way clustering. These alternative methods considerably improve upon the finite-sample overrejection problems.

I document the functionality of this command, including penalized least-squares estimation of group-specific coefficients and classification of unknown group membership under a certain number of groups; two lasso-type estimators with robust standard errors, namely classifier-lasso and postlasso; and determination of the number of groups based on a BIC-type information criterion. I further introduce postestimation commands to display and visualize the estimation results.

Reference:

Su L., Z. Shi, and P. C. B. Phillips. 2016. Identifying latent structures in panel data. Econometrica 84: 2215–2264.

This procedure is useful when data from different jurisdictions cannot be combined for analysis because of legal restrictions or confidentiality laws. Through Monte Carlo simulation studies, this procedure has been shown to be equivalent to a variation of the conventional DID model when data are poolable. The canonical DID implicitly assumes that the data for the treated group and the control group can be combined. The combined dataset is used to generate a post and treat dummy variables, which are then interacted to estimate the ATT. We also require “poolable” data to verify parallel trends, a key assumption of DID.

As a result, conducting DID analysis is nearly impossible using traditional methods when datasets are not combinable. The problem is pronounced for health economists, for whom legal restrictions in sharing administrative data can constrain DID analysis to learn of health systems. This package will make it easier for researchers who work with “unpoolable” data to conduct DID analysis. Furthermore, the package will also provide researchers with a plausibility check for pretreatment trends.

These types of data arise in many areas, including medical, epidemiological, economic, financial, and sociological studies. Ignoring interval-censoring will often lead to biased estimates.

A semiparametric Cox proportional hazards regression model is used routinely to analyze uncensored and right-censored event-time data. It is also appealing for interval-censored data because it does not require any parametric assumptions about the baseline hazard function. Also, under the proportional-hazards assumption, the hazard ratios are constant over time. Semiparametric estimation of interval-censored event-time data is challenging because none of the event times are observed exactly. Thus, “semiparametric” modeling of these data often resorted to using spline methods or piecewise-exponential models for the baseline hazard function. Genuine semiparametric modeling of interval-censored event-time data was not available until recent methodological advances, which are implemented in the stintcox command.

In this presentation, I will describe two basic formats for interval-censored data and will demonstrate how to fit the Cox model to these data using Stata's stintcox command. I will then demonstrate how to create time-varying covariates (TVCs) automatically using the stintcox command and how to use TVCs to test the proportional-hazards assumption. Last but not least, I will show how to incorporate TVCs in your predictions and plots of survivor and other functions.

Stata’s meta command offers full support for meta-analysis—from computing various effect sizes and producing a basic meta-analytic summary to performing tests for small-study effects. Stata 18 introduced support for meta-analysis of one proportion, meaning you can now use standard meta-analysis features such as forest plots and funnel plots with one-sample binary data. Stata 18 also introduced two new commands, meta meregress and meta multilevel, for performing multilevel meta-analysis. These commands allow you to analyze results from multiple studies in which the reported effect sizes are nested within higher-level groupings such as regions or schools. By properly accounting for the dependence among the effect sizes, we can produce more accurate inference.

In this presentation, I will demonstrate how to perform meta-analysis of proportions and multilevel meta-analysis in Stata 18. I will provide a brief introduction to meta-analysis and discuss effect sizes and confidence intervals relevant to prevalence data. For multilevel data, we’ll see how to include random intercepts and coefficients at different levels of hierarchy, perform sensitivity analysis, and assess the variability among the effect sizes at different levels of the hierarchy.

Relying on direct measures of women's bargaining power (as indicated by the preexisting literature), which include a woman's ability to make decisions about her own healthcare, large household purchases, small household purchases, visits to her family and friends, and contraceptive use, as well as measures of her autonomous financial capability. This measure of financial capability is then interacted with a woman's ability to make healthcare decisions solely or jointly with her husband to shed additional light on the influence that bargaining power has on the uptake of modern contraceptive methods. Having both financial capability and the sole ability to make healthcare decisions for herself increases a woman's probability of adopting modern contraceptive methods by 87%, and having sole responsibility over contraceptive decisions increases it by approximately 56%.

Using lasso as a robustness check, it is determined that the model is relatively well specified and has quite a large amount of explanatory power. Finally, the presentation uses a comparative analysis of spousal discord to demonstrate how spouses' often conflicting reports of intrahousehold decision making can impact key outcomes for women and finds that both spousal accord and the scenario in which the woman takes power are most effective for the adoption of modern contraception (leading to a 16.7% and 14.6% increase in the probability of using modern contraception, respectively).

Overall, the study finds that several aspects of a woman's household decision making and financial freedom, as well as the degree and directionality of spousal discord within her household impact her probability of adopting modern contraceptive methods.

I conducted an online survey targeting adults in sub-Saharan Africa, a continent characterized by lower levels of banking penetration, intensive use of cash, and popularity of mobile money accounts to overcome financial exclusion. I obtained robust evidence that the affect heuristic is the only relevant behavioral trait against the use of cash and of credit cards. This adds to criticisms of behavioral finance for frequently neglecting emotional drivers. Cognitive traits, such as mental accounting, fungibility bias, and habit do not mediate in the overall preference but in which contexts people prefer to use one payment method or another.

I find no behavioral drivers against the use of electronic payments but robust evidence that higher per capita income reduces their preference. All results are robust to alternative econometric specifications: multinomial logistic, ordered logistic, and logit regressions. My research provides a clear message for policy making: authorities might better favor ensuring that a wide variety of payment alternatives are available for people to use, including cash, and let them choose.

Primary focus is given to what other job skills are necessary for Stata users to become hired, be they software skills, soft skills, or general skills. Analyses of these jobs’ wages, required education levels, requisite job experience, titles and industries, and geography are also presented, as are trends in all of these findings. A separate sample of 164,973 worker profiles that mention Stata as a skill is then used to compare the demand and supply sides of this labor market. Results are meant to inform Stata users as to what they can expect on the job market, and perhaps most importantly, what additional skills they should gain to maximize their labor market outcomes or stand out among others competing for the same jobs.