Visualizing Data: R

Introduction to Econometrics, Fourth Edition, by James H. Stock and Mark W. Watson, provides an outstanding introduction to econometrics. Using an ingenious set of real-world questions and answers, they produced an excellent introduction to estimation, inference, and interpretation in econometrics.
The text also provides an excellent introduction to causal inference and explains the role of regression as a tool for it.
The fourth edition adds some key concepts and methods used in big-data analysis and machine learning.

Econometric Analysis has been the leading textbook for graduate econometrics in social science programs worldwide since 1990. It is also a major reference work for empirical research. Econometric Analysis ranked 34th with over 48,000 citations in Google Scholar's October 2014 Nature Journal list of the world's 100 all time most cited works. In 2022, with over 90,000 citations, Econometric Analysis is the most cited work ever written by an economist.

Unlike traditional texts, this book's practical yet professional approach demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions across a variety of disciplines. The author has organized the book's presentation around the type of data being analyzed with a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, the text incorporates more than 100 data sets in different formats. Updates introduce the latest developments in the field, including the recent advances in the so-called "causal effects" or "treatment effects" to provide a complete understanding of the impact and importance of econometrics today.

From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an irreverent guide to the essentials of econometrics The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jörn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. An irreverent review of econometric essentials A focus on tools that applied researchers use most Chapters on regression-discontinuity designs, quantile regression, and standard errors Many empirical examples A clear and concise resource with wide applications

An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences.

Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. The book introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.

The Stata edition of the groundbreaking textbook on data analysis and statistics for the social sciences and allied fields

This book is good for people who are completely new to coding and interested in learning the basics of R programming.

This book is a comprehensive guide to Bayesian analysis. The third edition includes R code for many of the examples, making it a valuable resource for learning Bayesian methods with R.