Although mainly intended for upper undergraduate and graduate students in the social sciences, given its applied nature, the book will equally appeal to readers from other disciplines, including the health sciences, statistics, engineering and computer science. Examples, conceptual exercises and exercises using software are featured throughout, together with case studies in Stata, mostly from the social sciences true to the book’s goal to facilitate the use of modern methods of data science in the field. It also explains how to construct n-gram variables from text data. In particular, the book presents logistic regression, regularized linear models such as the Lasso, nearest neighbors, the Naive Bayes classifier, classification trees, random forests, boosting, support vector machines, feature engineering, neural networks, and stacking. After introductory material on statistical learning concepts and practical aspects, each further chapter is devoted to a statistical learning algorithm or a group of related techniques. Review of Economics and Statistics Replication Files (Files to replicate all results from “Plausibly Exogenous” maintained by REStat.This textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical software Stata.The regression line depreciates the sum of. The file with the Stata code also includes sample data.) The standard error of the estimate is the estimation of the accuracy of any predictions. The code illustrates the basic procedure and may easily be modified for other data sets. Stata Code for IV sensitivity analysis (Stata code that produces some of the results from “Plausibly Exogenous” (with Tim Conley and Peter Rossi).(A big thanks to Damian Clarke for putting together this nice set of code.) Stata code for IV sensitivity analysis is available through Stata and can be installed in Stata by typingĭocumentation is available here. MATLAB code for finite sample inference for quantile regressionĬode for Sensitivity Analysis for IV (from “Plausibly Exogenous”).Stata Code for weak instrument robust inferenceĬode for Finite Sample Inference for Quantile Regressionīelow is a link to MATLAB code used to produce the results in Table 1 and Figure 1 in Chernozhukov, Hansen, and Jansson (2009) “Finite Sample Inference in Econometric Models via Quantile Restrictions.”.Due to this correction, the results produced by running the files given below will differ slightly from those in the published paper. ![]() I thank Mel Stephens for noticing a small error in the original code that has been corrected. The code illustrates the basic procedure and may easily be modified for other data sets and to provide inference that is robust to autocorrelation or clustering. The data are taken from Acemoglu, Johnson, and Robinson (2001) “The Colonial Origins of Comparative Development: An Empirical Investigation”. Along with the code, each file contains examples illustrating how the code may be implemented the data for the examples may also be downloaded below.Ĭode for Weak Instrument Robust Inferenceīelow are links for the Stata code and data used in the empirical example in “A Simple Approach to Heteroskedasticity and Autocorrelation Robust Inference with Weak Instruments” (with Victor Chernozhukov). Ridge regression may be useful if there is. ![]() The MATLAB code also includes code for performing the weak identification robust inference procedure proposed in “Instrumental Variable Quantile Regression: A Robust Inference Approach” (with Victor Chernozhukov). Lasso enables model selection in high dimensional data sets, selecting a minimal set of uncorrelated predictors. ado file that may be used to obtain LASSO and Post-LASSO estimates in Stata.īelow are links to MATLAB and Ox code for performing IVQR estimation and inference as developed in “Instrumental Quantile Regression Inference for Structural and Treatment Effect Models” (with Victor Chernozhukov) and “Instrumental Variable Quantile Regression” (with Victor Chernozhukov). Below are links Stata code and Matlab code for running the empirical examples from “ High-Dimensional Methods and Inference on Structural and Treatment Effects”.
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