Regression Discontinuity - Institute for Policy Research

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What to do (3) • Non-parametric approaches – What are non-parametric approaches? • Eliminates functional form assumptions • Performs a series of regressions within an interval, weighing observations closer to the boundary • Use local linear regression because it performs better at the boundaries – What depends on selecting correct bandwidth? • Key tradeoff in NP estimates: bias vs precision – How do you select appropriate bandwidth? – Ocular/sensitivity tests – Imbens & Kalyanaraman (2010) – Cross-validation methods » “Leave-one-out” method
What to do (4) 0 50 100 150 locpoly smooth: rawscor • State-of-art is imperfect • So we test for robustness and present multiple estimates -400 -200 0 200 400 cutmarg locpoly smooth: rawscor locpoly smooth: rawscor NJ PPVT LLR Plot New Jersey PPVT Parametric estimates Non-Parametric estimates Linear Quadratic Cubic 6 months 30 BW 40 BW (1) (2) (3) (4) (5) (6) 5.71* 5.37* 5.26 4.98* 8.09* 7.96* (1.44) (2.02) (2.72) (1.93) (3.86) (2.64)
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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
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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: What to do (2) • Parametric appro
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
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Typical estimation strategy for RDs
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Issue 1: To standardize or not stan
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Generalizing Treatment Effects Acro
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…Also Common in • Meta-Analysis
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What is Special about Pooling RD Es
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Benefits and Tradeoffs of Option 1
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Benefits and Tradeoffs of Option 2
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Distribution of Units in an RD with
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Multivariate RDD with Two Assignmen
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The Average Treatment Effect along
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Treatment Weights for τ MRD Weight
Page 145 and 146:
Estimating Treatment Effects Four P
Page 147 and 148:
Centering Approach Procedure allows
Page 149 and 150:
IV Approach (1) Rather than exclude
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Regression Discontinuity - Institute for Policy Research

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