Archive

Archive for 2022

Bayesian threshold autoregressive models

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…

Using the margins command with different functional forms: Proportional versus natural logarithm changes

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…

Comparing transmissibility of Omicron lineages

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…

Wharton Research Data Services, Stata 17, and JDBC

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…

Just released from Stata Press: A Visual Guide to Stata Graphics, Fourth Edition

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…