Josh Angrists’s undergraduate econometrics MIT’s OpenCourseWare includes lecture notes for many economics courses, including: The J-PAL MicroMasters Data Analysis for Social Scientists course covers probability and statistical analysis, with problem sets designed to increase data analysis skills using R. These posts typically summarize and then link to important papers on these topics. The World Bank has a series of methods blog posts on a wide range of topics. The guides do not provide a deep, comprehensive discussion of each topic but are good overviews and include sample R code. The MIT course 14.387 “Applied econometrics: Mostly harmless big data” roughly follows MHE, with slides from the fall 2014 course run posted on MIT OCW.ĮGAP methods guides include topics ranging from reading regression tables to causal inference, heterogeneous treatment effects, treatment effects, local average treatment effects, and covariate adjustment. The text also includes sample code for certain examples. Mostly Harmless Econometrics (MHE) is not RCT-specific but is an accessible applied econometrics text that covers the math and intuition behind decisions made in econometric analysis. Best for readers with some graduate-level econometrics. Includes some technical discussion and equations but can be understood by most readers.Īthey & Imbens (2017) The Econometrics of Randomized Experiments provides a more recent and more technical treatment of topics such as stratification and randomization inference. Useful R guides and tutorials include swirl, Base R cheat sheet, R-bloggers, and R Studio's resources.ĮGAP’s methods guides contain sample R code.ĭuflo et al.’s (2007) Using Randomization in Development Economics Research: A Toolkit is an accessible guide to using randomization in development economics. Useful Stata tutorials include J-PAL/IPA's Stata modules ( 101, 102, 103, 104 all direct download), Data analysis using Stata, and the Princeton DDS online tutorial.ĬSAE Coder’s Corner provides sample code (mostly Stata, though some R and Matlab) for procedures such as bootstrapping, adjusting standard errors for spatial correlation, and random forest. Stata has a list of resources for learning Stata, including Stata cheat sheets.
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