To instructors and students

This book is divided into three parts: foundations (chapters 1 to 4), regression analysis (chapters 5 to 10), and Advanced methods (chapters 11 to 14). Our graphical user interface (GUI) is designed for the second part. The source code can be found at https://github.com/besmarter/BSTApp. Instructors and students can access all the code, along with simulated and real datasets. There are three ways to install our GUI:

  1. Type shiny::runGitHub("besmarter/BSTApp", launch.browser=T) in the R console or any R code editor and execute it.
  2. Visit https://posit.cloud/content/4328505, log in or sign up for Posit Cloud, navigate to the BSTApp-master folder in the Files tab of the right-bottom window, then click on the app.R file and select Run App.
  3. Use a Docker image by typing in the Command Prompt:
  • docker pull magralo95/besmartergui:latest
  • docker run --rm -p 3838:3838 magralo95/besmartergui

Then users can access our GUI by going to http://localhost:3838/. See Chapter 5 for details.

Students should have a basic understanding of probability theory and statistics, as well as some background in econometrics and time series, particularly regression analysis. Familiarity with standard univariate and multivariate probability distributions is strongly recommended. See a nice summary of useful probability distributions in (Greenberg 2012).

Additionally, students who wish to master the material in this book should have programming skills in R software. An excellent starting point for R programming is the R Introduction Manual.

I have included both formal and computational exercises at the end of each chapter to help students gain a better understanding of the material presented. A solutions manual for these exercises accompanies this book.

Instructors can use this book as a textbook for a course on introductory Bayesian Econometrics/Statistics, with a strong emphasis on implementation and applications. This book is intended to be complementary, rather than a substitute, for excellent resources on the topic, such as Andrew Gelman et al. (2021), Chan et al. (2019), P. E. Rossi, Allenby, and McCulloch (2012), Greenberg (2012), John Geweke (2005), Lancaster (2004), and Koop (2003).

References

Chan, Joshua, Gary Koop, Dale J Poirier, and Justin L Tobias. 2019. Bayesian Econometric Methods. Vol. 7. Cambridge University Press.
Gelman, Andrew, John B Carlin, Hal S Stern, David Dunson, Aki Vehtari, and Donald B Rubin. 2021. Bayesian Data Analysis. Chapman; Hall/CRC.
———. 2005. Contemporary Bayesian Econometrics and Statistics. Vol. 537. John Wiley & Sons.
Greenberg, Edward. 2012. Introduction to Bayesian Econometrics. Cambridge University Press.
Koop, Gary M. 2003. Bayesian Econometrics. John Wiley & Sons Inc.
Lancaster, Tony. 2004. An Introduction to Modern Bayesian Econometrics. Blackwell Oxford.
Rossi, Peter E, Greg M Allenby, and Rob McCulloch. 2012. Bayesian Statistics and Marketing. John Wiley & Sons.