Stata User Group Meeting in Kyoto / ( / ) Stata User Group Meeting in Kyoto / 21
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1 Stata User Group Meeting in Kyoto / ( / ) Stata User Group Meeting in Kyoto / 21
2 Rosenbaum and Rubin (1983) logit/probit, ATE = E [Y 1 Y 0 ] ( / ) Stata User Group Meeting in Kyoto / 21
3 Rosenbaum and Rubin Y = { Y0 if W = 0 Y 1 if W = 1 ATE = E [Y 1 Y 0 ] = E [E [Y W = 1, X] E [Y W = 0, X]] ( / ) Stata User Group Meeting in Kyoto / 21
4 teffects psmatch( ) psmatch2 optmatch2 pscore ( / ) Stata User Group Meeting in Kyoto / 21
5 teffects psmatch. webuse cattaneo2,clear. teffects psmatch (bweight) (mbsmoke mmarried c.mage##c.mage fbaby medu) AI Robust bweight Coef. Std. Err. z P> z [95% Conf. Interval] ATE mbsmoke (smoker vs nonsmoker) ( / ) Stata User Group Meeting in Kyoto / 21
6 Abadie and Imbens (2012) ATE N ( ˆτ τ) d N ( 0, σ 2) (1) ( ) N ( ˆτ τ) d N 0, σ 2 c I 1 θ c (2) ( / ) Stata User Group Meeting in Kyoto / 21
7 psmatch2 teffects psmatch ATE. psmatch2 mbsmoke mmarried c.mage##c.mage fbaby medu, out(bweight) ties logit ate psmatch2 mbsmoke mmarried c.mage##c.mage fbaby medu, out(bweight) /// noreplacement logit ate ( / ) Stata User Group Meeting in Kyoto / 21
8 psmatch2 There are observations with identical propensity score values. The sort order of the data could affect your results. Make sure that the sort order is random before calling psmatch2. Variable Sample Treated Controls Difference S.E. T-stat bweight Unmatched ATT ATU ATE Note: S.E. does not take into account that the propensity score is estimated. ( / ) Stata User Group Meeting in Kyoto / 21
9 ATE bootstrap r(ate),reps(100):psmatch2 mbsmoke mmarried c.mage##c.mage /// fbaby medu, out(bweight) logit ate noreplacement Observed Bootstrap Normal-based Coef. Std. Err. z P> z [95% Conf. Interval] _bs_ ( / ) Stata User Group Meeting in Kyoto / 21
10 optmatch2 teffects psmatch psmatch2, 0.5(N), 0.6(S), 0.1(S), 0.9(N), = 0.9,, = 0.7 optmatch2, ( / ) Stata User Group Meeting in Kyoto / 21
11 optmatch2 optmatch2, optmatch2 isvar matcorr 2 ado ps. webuse cattaneo2,clear. logit mbsmoke mmarried c.mage##c.mage fbaby medu. predict ps ( / ) Stata User Group Meeting in Kyoto / 21
12 optmatch2. optmatch2 mbsmoke ps,gen(mid). drop if mid==.. save optdata,replace *. drop if mbsmoke==1. save nonsmg,replace ( / ) Stata User Group Meeting in Kyoto / 21
13 optmatch2 *. use optdata,clear. keep mid mbsmoke bweight. rename bweight bwsm. drop if mbsmoke==0. save smokeg,replace *mid 1:1. use nonsmg,clear. merge 1:1 mid using smokeg. ttest bweight==bwsm ( / ) Stata User Group Meeting in Kyoto / 21
14 optmatch2 Paired t test Variable Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] bweight bwsm diff mean(diff) = mean(bweight - bwsm) t = Ho: mean(diff) = 0 degrees of freedom = 863 Ha: mean(diff) < 0 Ha: mean(diff)!= 0 Ha: mean(diff) > 0 Pr(T < t) = Pr( T > t ) = Pr(T > t) = ( / ) Stata User Group Meeting in Kyoto / 21
15 . tebalance overlap ( / ) Stata User Group Meeting in Kyoto / 21
16 Subclassification Rosembaum and Rubin (1983) ATE τ = Var ( ˆτ) = K n k N [Ȳ 0k Ȳ 1k ] k=1 K k=1 ( nk N ) 2 Var [Ȳ0k Ȳ 1k ] ( / ) Stata User Group Meeting in Kyoto / 21
17 pscore pscore ATT ATT attr, attk, attnw, attnd, atts ( / ) Stata User Group Meeting in Kyoto / 21
18 pscore. webuse cattaneo2,clear. gen mage2=mageˆ2. pscore mbsmoke mmarried mage mage2 fbaby, /// pscore(pscore) blockid(myblock) numblo(2). atts bweight mbsmoke mmarried mage mage2 fbaby, /// pscore(pscore) blockid(myblock) boot reps(100) dots ( / ) Stata User Group Meeting in Kyoto / 21
19 pscore Bootstrapping of standard errors command: atts bweight mbsmoke mmarried mage mage2 fbaby, pscore(pscore) blockid(myblock) statistic: atts = r(atts)... >... note: label truncated to 80 characters Bootstrap statistics Number of obs = 4642 Replications = 100 Variable Reps Observed Bias Std. Err. [95% Conf. Interval] atts (N) (P) (BC) Note: N = normal P = percentile BC = bias-corrected ATT estimation with the Stratification method Bootstrapped standard errors n. treat. n. contr. ATT Std. Err. t ( / ) Stata User Group Meeting in Kyoto / 21
20 ATE S.E ttest unpaired t test teffects psmatch w.rep, tie psmatch2 boot w/o rep,no tie optmatch w/o rep,no tie pscore+atts w/o rep,no tie, ATT ( / ) Stata User Group Meeting in Kyoto / 21
21 teffects nnmatch teffects ra teffects ipw teffects aipw teffects ipwra ( / ) Stata User Group Meeting in Kyoto / 21
.001 nonsmoker smoker 0 Density 5.0e infant birthweight (grams) Graphs by 1 if mother smoked 図 2. 新生児体重のヒストグラム (
第一回はじめての傾向スコア分析 これから 3 回にわたって傾向スコア分析について説明します 各回の内容は以下の通りです 第一回はじめての傾向スコア分析第二回分析後のチェック第三回 Abadie and Imbens(2011) の貢献 Stata14 をまだお持ちでない方は是非 デモ版をダウンロードしてお試しください 理屈はともかく 一度 傾向スコア分析をやってみましょう 次に示すようにコマンド ウィンドウにコマンドを直接入力して
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80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = n λ x i e λ x i! = λ n x i e nλ n x i! n n log l(λ) = log(λ) x i nλ log( x i!) log l(λ) λ = 1 λ n x i n =
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87 6.1 AR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t 2, V(y t y t 1, y t 2, ) = σ 2 3. Thus, y t y t 1,
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