Instrumental Variables: Addressing Measurement Error and Reverse Causality

This lecture covers the use of instrumental variables to address measurement error and reverse causality in regression models. It explains how instrumental variables help correct biases caused by unobserved factors affecting both the independent and dependent variables. The discussion includes examples of how omitted variables and simultaneity can lead to inconsistent estimates. The lecture also delves into the concept of attenuation bias in OLS regression due to measurement error. Practical applications and solutions, such as the Cochrane-Orcutt transformation and Newey-West standard errors, are explored.