What is regression discontinuity method?

What is regression discontinuity method?

Regression Discontinuity Design (RDD) is a quasi-experimental evaluation option that measures the impact of an intervention, or treatment, by applying a treatment assignment mechanism based on a continuous eligibility index which is a variable with a continuous distribution.

What is the running variable in regression discontinuity?

The key to the RD design is that we have a deep understanding of the mechanism which underlies the assignment of treatment Di . In the sharp RD design this variable fully determines the treatment according to the cutoff rule: Di = ( 1 if Xi ≥ X0 0 if Xi < X0 . Xi is called the running variable.

What is spatial regression discontinuity?

Spatial regression discontinuity is a special case that recognizes geographic borders as sharp cutoff points. Geographic distance represents the assignment variable; treatment-defining border represents the cutoff. • Two-dimensional space (e.g., latitude and longitude) must be reduced to one one-dimensional distance.

How do you find regression discontinuity?

Regression Discontinuity: Simple Estimate

1. Model effect of D and X on Y by a regression Y=b0+τD+β1X+u.
2. Since D=1(X>c), this is same as Y=b0+τ1(X>c)+β1X+u.
3. Accounts for effect of X, if linear and D additive.
4. Very restrictive form.
5. Nonlinearity of effect of X.
6. Need a correct model of effect of X and D.

What is the regression discontinuity estimator?

Regression discontinuity (RD) analysis is a rigorous nonexperimental1 approach that can be used to estimate program impacts in situations in which candidates are selected for treatment based on whether their value for a numeric rating exceeds a designated threshold or cut-point.

When can you use regression discontinuity?

What is the identification assumption for regression discontinuity design?

Required assumptions. Regression discontinuity design requires that all potentially relevant variables besides the treatment variable and outcome variable be continuous at the point where the treatment and outcome discontinuities occur.

What is McCrary test?

– McCrary (2008) provides a formal test for manipulation of the assignment variable in an RD. The idea is that the marginal density of X should be continuous without manipulation and hence we look for discontinuities in the density around the threshold.

What are the assumptions for regression discontinuity?

What is a forcing variable?

Under an RD design, the effect on an intervention can be estimated as the difference in mean outcomes between treatment and comparison group units, adjusting statistically for the relationship between the outcomes and the variable used to assign units to the intervention, typically referred to as the “forcing” or “ …

Can a regression discontinuity be a problem in Rd?

However, it doesn’t sound like this is a regression discontinuity problem to me. In RD, you have a situation where you assigned some treatment based on a hard cutoff on a score. In your case, there could be an RD setup if people were rewarded for their job performance based on a cutoff in some performance score.

Are there any RD estimation packages for Stata?

In Stata, there are at least three user-written RD estimation packages: (1) Austin Nichols’s -rd- (ssc install rd); (2) CCT’s -rdrobust- (ssc install rdrobust); and Boris Kaiser’s -rdcv- (ssc install rdcv). A comparison of these will be presented in Part 2.

What are the advantages of using RDD in Stata?

Among the advantages of RDD are the weaker assumptions required for its validity compared to other non-experimental impact evaluation methods. For example, Hahn, Todd, and van der Klaauw (2001) showed that RDD requires milder assumptions relative to those needed for other non-experimental methods.