工具变量(IV)详细解说概要.pptx

IV IV 1、Origin Studying agricultural markets in the 1920s, the father and son research team of Phillip and Sewall Wright were interested in a challenging problem of causal inference: how to estimate the slope of supply and demand curves when observed data on prices and quantities are determined by the intersection of these two curves. In other words, equilibrium prices and quantities the only ones we get to observe solve these two stochastic equations at the same time. Upon which curve, therefore, does the observed scatterplot of prices and quantities lie? The fact that population regression coefficients do not capture the slope of any one equation in a set of simultaneous equations had been understood by Phillip Wright for some time. The IV method, first laid out in Wright (1928), solves the statistical simultaneous equations problem by using variables that appear in one equation to shift this equation and trace out the other. The variables that do he shifting came to be known as instrumental variables (Reiersol, 1941). 2、Work （1） Solving these two stochastic equations at the same time. (out date) （2） Causal inference . （3） Solving the problem of bias from measurement error in regression models. (4) Solving the problem of omitted variables bias.(most important ) IV causality First in a restricted model with constant effects. Second in a framework with unrestricted heterogeneous potential outcomes 4.1 IV and causality Chapter 4 IV First in a restricted model with constant effects. E.G. （IV） IV Q1：The second equality in (4.1.3) is useful because its usually easier to think in terms of regression coefficients than in terms of covariance. 2. Z S Y A First, the instrument must have a clear effect on . This is the first stage. Second, the only reason for the relationship between and is the first-stage. IV So where can you find an instrumental variable? One possible source of instruments for school