On the Identification of Misspecified Propensity Scores - School of ...

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FIGURE 1 E D P S Note: ––––- ˆαn p 1−p ˆ f0,n(p), - - - - - - - - ˆ f1,n(p) DGP: D ∗ = 1 + X1 + X2 + X1X2 − ǫi, ǫ ∼ N(0, σ 2 ), Estimated model: Q(X, β) = Φ(β 0 +β 1X1+β 2X2) Density estimated using the biweight kernel and Silverman’s rule of thumb, see Silverman[16]
FIGURE 2 E D P S Note: ––––- ˆαn p 1−p ˆ f0,n(p), - - - - - - - - ˆ f1,n(p) DGP: D ∗ = 1 + X1 + X2 + −ǫi, ǫ ∼ U(−1, 1), Estimated model: Q(X, β) = Φ(β 0 +β 1X1+β 2X2) Density estimated using the biweight kernel and Silverman’s rule of thumb, see Silverman[16]
Page 1 and 2: On the Identification of Misspecifi
Page 3 and 4: presented in this paper may be cons
Page 5 and 6: 3 The Restriction In order to ensur
Page 7 and 8: that misspecification is not very s
Page 9 and 10: where ˆεi := Yi − Q(Xi, ˆ θn)
Page 11 and 12: Throughout the simulations presente
Page 13 and 14: 5 Conclusion [TABLE 8 ABOUT HERE] [
Page 15 and 16: Ichimura, and Todd [6]. In addition
Page 17 and 18: To check the second condition of th
Page 19 and 20: where M = sup θ∈Θ[D−Q(x, θ)]
Page 21: [15] S, B. (2004): “An Evaluation
Page 25 and 26: TABLE 2: P c=0.05 Proportion of Rej

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