Introduction

Some discussions have arisen lately with regard to which standard errors should be used by practitioners in the presence of heteroskedasticity in linear models. The discussion intrigued me, so I took a second look at the existing literature. I provide an overview of theoretical and simulation research that helps us answer this question. I also present simulation results that mimic or expand some of the existing simulation studies. I’ll share the Stata code I used for the simulations in hopes that it might be useful to those that want to explore how the various standard-error estimators perform in situations that are relevant to your research. Read more…

Stata Press is pleased to announce the release of Microeconometrics Using Stata, Second Edition, Volumes I and II, by A. Colin Cameron and Pravin K. Trivedi. This book not only debuted as Kindle’s #1 New Release but also immediately ranked high on Kindle’s competitive best-seller lists in categories such as Statistics, Microeconomics, Econometrics & Statistics, Education Software, Education Statistics, and Mathematical & Statistical. Read more…

Autoregressive (AR) models are some of the most widely used models in applied economics, among other disciplines, because of their generality and simplicity. However, the dynamic characteristics of real economic and financial data can change from one time period to another, limiting the applicability of linear time-series models. For example, the change of unemployment rate is a function of the state of the economy, whether it is expanding or contracting. A variety of models have been developed that allow time-series dynamics to depend on the regime of the system they are part of. The class of regime-dependent models include Markov-switching, smooth transition, and threshold autoregressive (TAR) models. Read more…

margins is a powerful tool to obtain predictive margins, marginal predictions, and marginal effects. It is so powerful that it can work with any functional form of our estimated parameters by using the expression() option. I am going to show you how to obtain proportional changes of an outcome with respect to changes in the covariates using two different approaches for linear and nonlinear models. Read more…

Monitoring lineages of the Omicron variant of the SARS-CoV-2 virus continues to be an important health consideration. The World Health Organization identifies BA.1, BA.1.1, and the most recent BA.2 as the most common sublineages. A recent study from Japan, Yamasoba et al. (2022), compares, among other characteristics, the transmissibility of these three Omicron lineages with the latest Delta variant. It identifies BA.2 to have the highest transmissibility of the four. Preprint of the study is available at bioarxiv.org. One interesting aspect of the study is the application of Bayesian multilevel models for representing lineage growth dynamics. In this post, I demonstrate how to use Stata’s bayesmh and bayesstats summary commands to perform similar analysis. Read more…

Working with Wharton Research Data Services (WRDS) data in Stata is now even easier. I previously wrote about accessing WRDS data via ODBC. With Stata 17, using JDBC makes configuring WRDS and Stata even easier—and the steps to configure are the same across all operating systems. Whether you download WRDS data to your local machine or work in the cloud, the command to use in Stata for JDBC is jdbc. Read more…

Stata Press is pleased to announce the release of A Visual Guide to Stata Graphics, Fourth Edition by Michael N. Mitchell. This book debuted as Kindle’s #1 New Release in the category Mathematical & Statistical and continues to appear on Kindle’s best-seller list in numerous categories, including Educational Software and Education Statistics. Read more…