Emphasizes econometric theory, methods, and applications using regression, instrumental variables, differences-in-differences, regression discontinuity designs, machine learning and big data sets, and problems related to standard errors and statistical inference.
We then discuss in detail a number of econometric techniques aimed at addressing endogeneity problems, including instrumental variables, difference-in-differences estimators, regression discontinuity design, matching methods, panel data methods, and higher order moments estimators.
These include randomized experiments, and quasi-experimental approaches including the regression-discontinuity design, instrumental variables, and difference-in-differences. The article provides examples from the recent empirical literature in the economics of education.
The search strategy, which was adapted for each database, combined and interacted the terms "cost*", "effect*", "benefit*", "cost-effective*" and "cost-benefit*", with "matching", "stratification", "regression*", "propensity score*", "instrumental variable*", "difference in difference*", "control function" and "discontinuity".
The first quarter of the course will cover common statistical methods in applied microeconomics, including instrumental variable models, regression discontinuities, difference-in-differences, and randomized inference.
This course provides an introduction to large-sample casual inference using standard techniques: regression (parametric and nonparametric), matching, weighting and doubly robust techniques, instrumental variables, fixed effects and difference-in-differences. The course also covers methods of statistical estimation used for these approaches.
Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models.
Studies that satisfied the eligibility criteria exemplified the use of matching, regression analysis, propensity scores, instrumental variables, as well as difference-in-differences approaches.
Such analytical approaches operate in the context of the potential outcomes framework and include matching, regression analysis, propensity scores, instrumental variables, regression discontinuity designs, difference-in-differences approaches and control functions [6].
This is an OT analysis of an RCT that was originally powered to detect differences in instrumental vaginal births, based on an arbitrary reduction of instrumental births from 15%to9%9% (α = 0.05, ß = 0.2).
