Regression-Discontinuity Design - Institute for Policy Research

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Specification of Functional Form Parametric approaches (2) Function al <strong>for</strong>m Parametric models used in analysis Linear Quad Cubic 6 mnth s (1) (2) (3) (4) (5) PPVT Quad 0.33 -1.91 -3.18 -3.74 (2.49) (4.15) (5.62) (6.10) Student covariates Yes Yes Yes Yes Fuzzy cases No No No No
Specification of Functional Form Non‐parametric approaches Parametric models used in analysis Nonparametric estimates by bandwidth Function al <strong>for</strong>m Linear Quad Cubic 6 mnths 50 BW 75 BW (1) (2) (3) (4) (5) (6) (7) PPVT Quad 0.33 -1.91 -3.18 -3.74 -4.99 -1.66 (2.49) (4.15) (5.62) (6.10) (4.40) (3.91) Student covariates Yes Yes Yes Yes No No Fuzzy cases No No No No No No
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Regression‐Discontinuity Design D
Page 3 and 4:
Design Overview Design Examples Vis
Page 5 and 6:
Real Examples of RD Reading First b
Page 7 and 8:
RDD Visual Depiction Comparison Tre
Page 9 and 10:
Two Rationales for RDD 1. Selection
Page 11 and 12:
Two Rationales for RDD 1. Selection
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1. Overrides to the cutoff: Sharp,
Page 15 and 16:
Fuzzy RD Designs • Requires a dis
Page 17 and 18:
Requirements for Estimating Treatme
Page 19 and 20:
Implementation Threats to RD Design
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Example: AYP Data from Texas Densit
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What to do? • This is a big probl
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Analytic Threats to RD Design: Miss
Page 27 and 28:
Nonlinearities in Functional Form
Page 29 and 30:
Here we see a discontinuity between
Page 31 and 32:
Functional Form: Interactions • S
Page 33 and 34: If we superimpose the regression li
Page 35 and 36: Here we see an example where the tr
Page 37 and 38: Adding Nonlinear Terms to the Model
Page 39 and 40: Adding Nonlinear and Interaction Te
Page 41 and 42: Adding Terms, Continued • When in
Page 43 and 44: Example of Parametric Plot used aga
Page 45 and 46: Non‐parametric Approach lim x↓c
Page 47 and 48: Non‐parametric plot Local linear
Page 49 and 50: Semi‐Parametric Plot RD data from
Page 51 and 52: What to do? • Check to see whethe
Page 53 and 54: Shadish, Galindo, Wong, V., Steiner
Page 55 and 56: RD Design assumption 2 • Continui
Page 57 and 58: Implementation assumption 2 • No
Page 59 and 60: Analytic assumption 1 continued •
Page 61 and 62: Shadish et al. (under review) Vocab
Page 63 and 64: RDD Example 2
Page 65 and 66: Best case scenario - regression lin
Page 67 and 68: Correct specification of functional
Page 69 and 70: What to do (2) • Parametric appro
Page 71 and 72: What to do (4) • State‐of‐art
Page 73 and 74: What to do (5) • Move to a tie br
Page 75 and 76: Example Cocaine Project • Include
Page 77 and 78: What to do (7) • Estimation throu
Page 79 and 80: 2. Dealing with Overrides to the cu
Page 81 and 82: 2. Overrides to the cutoff • Mult
Page 83: Specification of Functional Form Pa
Page 87 and 88: Overrides to cutoff (2) Alternative
Page 89 and 90: Final estimates Function al form Pa
Page 91 and 92: Addressing Potential Issues with us
Page 93 and 94: IS Wong et al. a Fallible RD or Ins
Page 95 and 96: African‐American Home language Sp
Page 97 and 98: Fallible RD? • Now we look for di
Page 99 and 100: McCrary Test for New Jersey
Page 101 and 102: Summarizing Checks - Conditional Me
Page 103 and 104: Other Kinds of Threats with RD 1. L
Page 105 and 106: Variance of Impact Estimator in RA
Page 107 and 108: Variance of Impact Estimator in RDD
Page 109 and 110: Power Considerations Unique to RDD
Page 111 and 112: Generalizing Beyond The Cut‐off I
Page 113 and 114: Observed vs Counterfactual Outcomes
Page 115 and 116: Questions About Generalizing From R
Page 117 and 118: What Do We Mean By Comparison Group
Page 119 and 120: Our Purpose • Use experimental da
Page 121 and 122: What Is The Target Parameter? • A
Page 127 and 128: What About The Above The Cut‐Off
Page 129 and 130: Root Mean Square Error In Experimen
Page 131 and 132: Post-Treatment Medicaid Expenditure
Page 133 and 134: Results For Number of Unpaid Worker
Page 135 and 136:
Results For Expenditures With Expen
Page 137 and 138:
Conclusions re Using a Comparison R
Page 139 and 140:
Implications for Practice • Which
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RD with Multiple Cutoffs Design (as
Page 143 and 144:
Analyzing RDs with Multiple Cutoffs
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Examples • Black, Galdo, and Smit
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Issue 2: To pool or not pool across
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Pooling is Already Common in Social
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What is Special about Pooling RD Es
Page 153 and 154:
Estimating Treatment Effects across
Page 155 and 156:
Estimating Treatment Effects across
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Generalizing Across Multiple Assign
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Multivariate RDD with Two Assignmen
Page 161 and 162:
Recent Education Examples of RDDs w
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Frontier‐specific Effect (τ R )
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Requirements for a valid Multivaria
Page 167 and 168:
Frontier Approach Estimates the dis
Page 169 and 170:
Univariate Approach Addresses dimen
Page 171 and 172:
Monte Carlo Study Wong, V., Steiner
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Implications for Practice • Which
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