Regression Discontinuity - Institute for Policy Research

More documents

Recommendations

Info

Univariate Approach Addresses dimensionality problem by estimating treatment effects for each frontier separately. First, exclude all observations with r values less than its respective cutoff (r c ), and choosing a single assignment variable (say, m) and cutoff (m c ). Second, estimate treatment effects by measuring size of discontinuity of the conditional outcomes at the cutoff for the designated assignment variable using parametric, semi-parametric, or nonparametric approaches Estimates τ R or τ M
IV Approach (1) Rather than exclude observations assigned to treatment by alternative mechanisms, delegate these cases as “fuzzy” units. First, designate a treatment assignment mechanism serves as the instrument for treatment receipt. Second, estimate the local average treatment effect, which is the difference in conditional mean outcomes for treatment and comparison groups divided by the difference in treatment receipt rates for both groups within a neighborhood around the cutoff.
Page 1 and 2:
Regression-Discontinuity Design Day
Page 3 and 4:
Design Overview Design Examples Vis
Page 5 and 6:
RDD Visual Depiction Comparison
Page 7 and 8:
RDD Visual Depiction Discontinuity,
Page 9 and 10:
Two Rationales for RDD 1. Selection
Page 11 and 12:
Required Assumptions for RD design
Page 13 and 14:
Sharp RD Designs • Requires that
Page 15 and 16:
Estimating Treatment Effects in Fuz
Page 17 and 18:
“Non”-RD designs • No discont
Page 19 and 20:
2. Manipulation of the assignment v
Page 21 and 22:
Diagnostic test • Can perform “
Page 23 and 24:
Analytic Threats to RD Design: Para
Page 25 and 26:
The size of the discontinuity at th
Page 27:
Functional Form • Put more techni
Page 30 and 31:
Functional Form: Interactions • S
Page 32 and 33:
If we superimpose the regression li
Page 34 and 35:
Here we see an example where the tr
Page 36 and 37:
Adding Nonlinear Terms to the Model
Page 38 and 39:
Adding Nonlinear and Interaction Te
Page 40 and 41:
Adding Terms, Continued • When in
Page 42 and 43:
Example of Parametric Plot used aga
Page 44 and 45:
Non-parametric Approach lim x¯c E[
Page 46 and 47:
3. Semi-Parametric Approach Y i = t
Page 48 and 49:
Problem of Incorrect Bandwidth Sele
Page 50 and 51:
RDD Example 1
Page 52:
RD Design assumption 1 • Probabil
Page 55 and 56:
Implementation assumption 2 • No
Page 57 and 58:
Analytic assumption 1 continued •
Page 59 and 60:
Shadish et al. (2011) Vocabulary RD
Page 61 and 62:
RDD Example 2
Page 63 and 64:
Best case scenario - regression lin
Page 65 and 66:
Correct specification of functional
Page 67 and 68:
What to do (2) • Parametric appro
Page 69 and 70:
What to do (4) 0 50 100 150 locpoly
Page 71 and 72:
What to do (5) • Move to a tie br
Page 73 and 74:
Example Cocaine Project • Include
Page 75 and 76:
What to do (7) • Estimation throu
Page 77 and 78:
2. Dealing with Overrides to the cu
Page 79 and 80:
2. Overrides to the cutoff • Mult
Page 81 and 82:
Specification of Functional Form Pa
Page 83 and 84:
Specification of Functional Form No
Page 85 and 86:
Overrides to cutoff (2) Alternative
Page 87 and 88:
Final estimates Function al form Pa
Page 89 and 90:
Addressing Potential Issues with us
Page 91 and 92:
African-American Home language Span
Page 93 and 94:
What about cutoff Shift in Number o
Page 95 and 96:
McCrary Test for New Jersey
Page 97 and 98: Take Home Message • Like RE, the
Page 99 and 100: Inadequate statistical power • Ne
Page 101 and 102: Power Considerations Common to Rand
Page 103 and 104: What “Uniquely” Affects Power i
Page 105 and 106: Generalizing Beyond The Cut-off In
Page 107 and 108: Adding an Untreated RD Comparison F
Page 109 and 110: Creating A Regression Discontinuity
Page 111 and 112: Pretest and Posttest RD • Imagine
Page 113 and 114: What Is The Target Parameter? • A
Page 115 and 116: Table 1: Bias At the Cutoff Adding
Page 117 and 118: Summary of what the Pretest- Supple
Page 119 and 120: Additional Topic not covered in Cla
Page 121 and 122: Sample Data with Multiple Cutoffs O
Page 123 and 124: Typical estimation strategy for RDs
Page 125 and 126: Issue 1: To standardize or not stan
Page 127 and 128: Generalizing Treatment Effects Acro
Page 129 and 130: …Also Common in • Meta-Analysis
Page 131 and 132: What is Special about Pooling RD Es
Page 133 and 134: Benefits and Tradeoffs of Option 1
Page 135 and 136: Benefits and Tradeoffs of Option 2
Page 137 and 138: Distribution of Units in an RD with
Page 139 and 140: Multivariate RDD with Two Assignmen
Page 141 and 142: The Average Treatment Effect along
Page 143 and 144: Treatment Weights for τ MRD Weight
Page 145 and 146: Estimating Treatment Effects Four P
Page 147: Centering Approach Procedure allows
show all

Regression Discontinuity - Institute for Policy Research

Create successful ePaper yourself

Delete template?

Save as template?