下面这种图表非常直观，有时候一张图的效果胜过千言万语，甚至比几张表格还要有效。例如，在完成基准回归分析之后，进行稳健性检验、异质性分析、安慰剂检验等进一步分析时，完全可以将这些回归结果汇总在一张图表中。这样做不仅便于与基准回归结果进行比较，还能清晰地展示它们之间的差异和相似之处。
除此之外，也可以参看：以图形方式呈现异质性分析结果, 留下一个较直观的印象！
*群友可前往社群下载完整数据和代码.
**计量经济圈已调试，可以直接运行出结果，当然中间需要下载一些命令，比如gtools。 use "D:/下载/129581-V1/Kit/data/main_work", clear gen post = yearsopen10>=0 xi i.post*patent_lib, noomit xtset identifier yearsopen10 cem pat_ID(#0) stateid(#0), treatment(treated) drop if cem_matched==0 * Baseline eststo r0: xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights], fe cluster(pat_ID) sum pat_pop_distance estadd scalar mean_scalar = r(mean) * Add matching on university library drop uni_library gen uni_library=regexm(ParentInstitutionofLibrary, "University") gen control_uni=regexm(LibraryType, "Academic") replace uni_library = 0 if control_uni==0 xi i.post*patent_lib, noomit xtset identifier yearsopen10 preserve cem pat_ID(#0) stateid(#0) uni_library(#0), treatment(treated) drop if cem_matched==0 eststo r3: xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights], fe cluster(pat_ID) sum pat_pop_distance estadd scalar mean_scalar = r(mean) restore * Add matching on patenting per capita preserve bysort pat_ID identifier: egen sim_size=sum((yearsopen10<0)*pat_pop_distance) cem pat_ID(#0) stateid(#0) uni_library(#0) sim_size(#5), treatment(treated) drop if cem_matched==0 eststo r4: xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights], fe cluster(pat_ID) sum pat_pop_distance estadd scalar mean_scalar = r(mean) restore * Add matching on patenting per capita and on population preserve bysort pat_ID identifier: egen sim_size=sum((yearsopen10<0)*pat_pop_distance) bysort pat_ID identifier: egen sim_size_pop=sum((yearsopen10<0)*pop_15m) cem pat_ID(#0) stateid(#0) uni_library(#0) sim_size(#5) sim_size_pop(#5), treatment(treated) drop if cem_matched==0 eststo r5: xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights], fe cluster(pat_ID) sum pat_pop_distance estadd scalar mean_scalar = r(mean) restore * Distance match: < 100 miles use "D:/下载/129581-V1/Kit/data/main_work_distance_matched", clear keep if pat_FDL_dist<=100 | pat_FDL_dist==. gen post = yearsopen10>=0 xi i.post*patent_lib, noomit xtset identifier yearsopen10 cem pat_ID(#0), treatment(treated) drop if cem_matched==0 eststo r6: xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights], fe cluster(pat_ID) sum pat_pop_distance estadd scalar mean_scalar = r(mean) * Distance match: 5 closest use "D:/下载/129581-V1/Kit/data/main_work_distance_matched", clear replace pat_FDL_dist = -pat_FDL_dist bysort pat_ID yearsopen patent_lib : egen rank = rank(pat_FDL_dist) if pat_FDL_dist!=0, track keep if rank<6 | rank==. egen yearID = group(yearsopen10) gen post = yearsopen10>=0 xi i.post*patent_lib, noomit xtset identifier yearsopen10 cem pat_ID(#0), treatment(treated) drop if cem_matched==0 eststo r7: xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights], fe cluster(pat_ID) sum pat_pop_distance estadd scalar mean_scalar = r(mean) * Synthetic
u "D:/下载/129581-V1/Kit/data/patent_characteristics", clear drop if appln_y>2010 drop if publn_y>2010 drop if lat<23 drop if lon<-130 replace appln_id = inpadoc_family_id gen summer_other=1 keep appln_id appln_y summer_other gduplicates drop collapse (sum) summer_other, by(appln_y) save "D:/下载/129581-V1/Kit/data/patent_no_by_year.dta", replace use "D:/下载/129581-V1/Kit/data/main_work", clear keep if patent_lib==1 duplicates drop pat_ID yearsopen10, force drop patent_lib merge m:1 appln_y using "D:/下载/129581-V1/Kit/data/patent_no_by_year.dta" keep if _merge==3 gen share = pat_15m/(summer_other) replace share = . if yearsopen10!=-1 bysort pat_ID: egen mean_share = sum(share) gen pat_pop_distance_0 = (mean_share*(summer_other))/pop_15m*100000 ren pat_pop_distance pat_pop_distance_1 reshape long pat_pop_distance_, i(pat_ID yearsopen10) j(patent_lib) gen post = yearsopen10>=0 xi i.post*patent_lib, noomit drop identifier egen identifier = group(pat_ID patent_lib) xtset identifier yearsopen10 eststo rs: xtreg pat_pop_distance_ i.yearID2 _IposXpaten_1 post patent_lib , fe cluster(pat_ID) sum pat_pop_distance_ estadd scalar mean_scalar = r(mean)
erase "D:/下载/129581-V1/Kit/data/patent_no_by_year.dta" * Test of SUTVA use "D:/下载/129581-V1/Kit/data/main_work", clear cap drop strata_id mean drop if patent_lib==1 bys pat_ID: egen cf=min(pat_FDL_dist) replace patent_lib=(pat_FDL_dist==cf) replace treated = patent_lib egen strata_id = group(pat_ID stateid) bysort strata_id: egen mean = mean(treated) drop if mean==0 | mean==1 keep if yearsopen10>=-5 & yearsopen10<=5 gen post = yearsopen10>=0 xi i.post*patent_lib, noomit xtset identifier yearsopen10 cem pat_ID(#0) stateid(#0), treatment(treated) drop if cem_matched==0 eststo r2: xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights], fe cluster(pat_ID) sum pat_pop_distance estadd scalar mean_scalar = r(mean) * Distance use "D:/下载/129581-V1/Kit/data/main_work", clear gen post = yearsopen10>=0 xi i.post*patent_lib, noomit xtset identifier yearsopen10 cem pat_ID(#0) stateid(#0), treatment(treated) drop if cem_matched==0 foreach x in 50 100 { eststo rd`x': xtreg pat_pop_`x'm _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights] , fe cluster(pat_ID) sum pat_pop_`x'm estadd scalar mean_scalar = r(mean) } label var _IposXpaten_1 "Pat Lib x Post " label var post "Post " label var patent_lib "Patent Library"
esttab r0 r3 r4 r5 r6 r7 rs r2 r0 rd50 rd100 using "D:/下载/129581-V1/Kit/results/TabB2.tex", posthead("") keep( _IposXpaten_1 post )order( post _IposXpaten_1 ) fragment compress nogaps nodepvar noisily /// stats(mean_scalar r2 N, fmt(%9.1f %9.2f %18.0g) labels(`"Mean Dep."' `"R2 (within)"' `"Obs."')) nonotes nonumber nomtitle b(1) se(1) obslast /// star(* 0.10 ** 0.05 *** 0.01) label replace
mat b= r(coefs) cap drop b se counter gen b = . gen se = . gen counter = . forvalues i = 1(1)11{ local j = (`i'*3)-2 local k = `j'+1 replace b = b[2,`j'] in `i' replace se = b[2,`k'] in `i' replace counter = `i' in `i' } gen upper = b + 1.68*se gen lower = b - 1.68*se replace counter = (counter*-1)+12.5 gen significant = upper<0 | lower>0 replace lower=. if upper==. replace counter = -4 if counter == 1.5 replace counter = -3 if counter == 2.5 replace counter = -2 if counter == 3.5 replace counter = 0.5 if counter == 4.5 replace counter = 2 if counter == 5.5 replace counter = 3.5 if counter == 6.5 replace counter = 4.5 if counter == 7.5 replace counter = 7 if counter == 8.5 replace counter = 8 if counter == 9.5 replace counter = 9 if counter == 10.5 gen mlabel = "" replace mlabel = "Increase by 3.2 / 18% relative to baseline" if counter == 11.5 replace mlabel = "2.9 / 17%" if counter == 9 replace mlabel = "2.4 / 19%" if counter == 8 replace mlabel = "2.4 / 20%" if counter == 7 replace mlabel = "1.9 / 8%" if counter == 4.5 replace mlabel = "3.3 / 16%" if counter == 3.5 replace mlabel = "2.4 / 12%" if counter == 2 replace mlabel = "-0.5 / -3%" if counter == 0.5 replace mlabel = "3.2 / 18%" if counter == -2 replace mlabel = "2.1 / 12%" if counter == -3 replace mlabel = "-1.4 / -7%" if counter == -4 twoway (rcap upper lower counter, horizontal color(edkblue)) (scatter counter b if significant==1, color(red) msymbol(d) mlabel(mlabel) mlabcolor(black) mlabsize(2) mlabp(6) ) (scatter counter b if significant==0, color(edkblue) msymbol(dh) mlabel(mlabel) mlabcolor(black) mlabsize(2) mlabp(6)), ytitle("") legend(off) xline(0) ylabel(-4 "11) in 50-100 mi" -3 "10) in 15-50 mi" -2 "9) <= 15 mi" -1 "{bf:Patents p.c. in...}" 0.5 "8) Pseudo opening" 2 "7) Synthetic libraries" 3.5 "6) 5 closest" 4.5 "5) <100 mi" 5.5 "{bf:Distance Match...}" 7 "4) + Population" 8 "3) + Patent p.c." 9 "2) +University" 10 "{bf:Add matching on...}" 11.5 "1) Baseline", angle(0) ) ysize(6) xline(3.216079, lc(gs12)) yscale(range(-4.5 11.5)) graph export "results\Fig6.pdf", replace
**计量经济圈已调试，可以直接运行，当然中间需要下载一些命令，比如labmask。 use "D:/下载/129581-V1/Kit/data/main_work", clear egen yearID = group(yearsopen10) gen post = yearsopen10>=0 xi i.post*patent_lib, noomit xtset identifier yearsopen10 cem pat_ID(#0) stateid(#0) $matched, treatment(treated) drop if cem_matched==0 gen b = . gen se = . gen subcategory = "" gen counter = . ren pat_pop_distance pat_mainarea30_0 unab k: pat_mainarea30_* local i = 1 foreach x in `k' { quietly{ xtreg `x' _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights], fe cluster(pat_ID) replace b = _b[_IposXpaten_1] in `i' replace se = _se[_IposXpaten_1] in `i' replace subcategory = "`x'" in `i' replace counter = `i' in `i' local i = `i'+1 } } keep if counter!=. gen upper = b + 1.68*se gen lower = b - 1.68*se replace subcategory = subinstr(subcategory, "pat_mainarea30_", "", .) destring subcategory, replace ren subcategory mainarea30 replace counter = (counter*-1)+37 joinby mainarea30 using "D:/下载/129581-V1/Kit/data/mainarea35_area35", unmatched(master) drop mainarea30 _merge joinby area30 using "D:/下载/129581-V1/Kit/data/mainarea35_area35", unmatched(master) replace mainarea30=0 if mainarea30==. keep b se mainarea30 counter upper lower duplicates drop sort mainarea30 decode mainarea30, gen(label_ex) cap drop counter drop if upper==. gsort - b gen counter = _n replace counter = 7-counter labmask counter, values(label_ex) gen significant = upper<0 | lower>0 replace lower=. if upper==. twoway (rcap upper lower counter, horizontal color(edkblue)) (scatter counter b if significant==1, color(red) msymbol(d)) (scatter counter b if significant==0, color(edkblue) msymbol(dh)), ytitle("") legend(off) xline(0) ylabel(0 1 2 3 4 5 6 "Baseline" , valuelabel ) ysize(4) graph export "results\Fig7.pdf", replace
use "D:/下载/129581-V1/Kit/data/main_work", clear
egen yearID = group(yearsopen10)
gen post = yearsopen10>=0
xi i.post*patent_lib, noomit
xtset identifier yearsopen10
cem pat_ID(#0) stateid(#0), treatment(treated)
drop if cem_matched==0
egen group_year = group(appln_y pat_ID)
gen b = .
gen se = .
gen name = ""
gen counter = 0
local i = 1
foreach x in pat_pop_distance pat_independent_pc pat_company_pc pat_uni_hosp_pc pat_non_profit_pc {
xtreg `x' _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights],fe cluster(pat_ID)
sum `x'
estadd scalar mean_scalar = r(mean)
replace b = _b[ _IposXpaten_1] in `i'
replace se = _se[ _IposXpaten_1] in `i'
replace counter = `i' in `i'
replace name = "`x'" in `i'
local i = `i'+1
}
xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights] if hist_high==1,fe cluster(pat_ID)
sum pat_pop_distance if hist_high==1
estadd scalar mean_scalar = r(mean)
replace b = _b[ _IposXpaten_1] in `i'
replace se = _se[ _IposXpaten_1] in `i'
replace counter = `i' in `i'
replace name = "High" in `i'
local i = `i'+1
xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights] if hist_high==0, fe cluster(pat_ID)
sum pat_pop_distance if hist_high==0
estadd scalar mean_scalar = r(mean)
replace b = _b[ _IposXpaten_1] in `i'
replace se = _se[ _IposXpaten_1] in `i'
replace counter = `i' in `i'
replace name = "Low" in `i'
local i = `i'+1
bysort pat_ID: egen uni_pat_lib = max(patent_lib*uni_library)
xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights] if uni_pat_lib==1 , fe cluster(pat_ID)
sum pat_pop_distance if uni_library==1
estadd scalar mean_scalar = r(mean)
xtreg pat_pop_distance _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights] if uni_pat_lib==0 , fe cluster(pat_ID)
sum pat_pop_distance if uni_library==0
estadd scalar mean_scalar = r(mean)
gen upper = b + 1.68*se
gen lower = b - 1.68*se
replace counter = (counter*-1)+8
gen significant = upper<0 | lower>0
twoway (rcap upper lower counter, horizontal) (scatter counter b if significant==1, color(red) msymbol(d)) (scatter counter b if significant==0, color(edkblue) msymbol(dh)), ytitle("") ysize(4) legend(off) xline(0) ylabel(1 "Historically Low Patenting Regions" 2 "Historically High Patenting Regions" 3 "Patents assigned to Government / Military / Non-Profit" 4 "Patents assigned to Universities" 5 "Patents assigned to Companies" 6 "Patents assigned to Individual Inventor" 7 "Baseline" , valuelabel labsize(small))
graph export "D:/下载/129581-V1/Kit/results/FigB3.pdf", replace
use "D:/下载/129581-V1/Kit/data/main_work", clear egen yearID = group(yearsopen10) gen post = yearsopen10>=0 xi i.post*patent_lib, noomit xtset identifier yearsopen10 cem pat_ID(#0) stateid(#0) $matched, treatment(treated) drop if cem_matched==0 gen b = . gen se = . gen subcategory = "" gen counter = . unab k: pat_area30_* local i = 1 foreach x in `k' { quietly{ xtreg `x' _IposXpaten_1 post patent_lib i.yearID2 [aweight=cem_weights], fe cluster(pat_ID) replace b = _b[_IposXpaten_1] in `i' replace se = _se[_IposXpaten_1] in `i' replace subcategory = "`x'" in `i' replace counter = `i' in `i' local i = `i'+1 } } keep if counter!=. gen upper = b + 1.68*se gen lower = b - 1.68*se replace subcategory = subinstr(subcategory, "pat_area30_", "", .) destring subcategory, replace ren subcategory area30 replace counter = (counter*-1)+37 merge m:1 area30 using "D:/下载/129581-V1/Kit/data/area30_label", update cap drop counter drop if upper==. gsort - area30 gen counter = _n replace counter = counter+2 if counter>2 replace counter = counter+2 if counter>10 replace counter = counter+2 if counter>17 replace counter = counter+2 if counter>27 replace counter = counter+2 if counter>33 set obs 36 replace counter = 3 in 31 replace counter = 11 in 32 replace counter = 18 in 33 replace counter = 28 in 34 replace counter = 34 in 35 replace counter = 41 in 36 replace label_ex = "Other Fields" if counter==3 replace label_ex = "Mechanical Engineering" if counter==11 replace label_ex = "Process Engineering" if counter==18 replace label_ex ="Chemistry" if counter==28 replace label_ex = "Instruments" if counter==34 replace label_ex = "Electrical Engineering" if counter==41 labmask counter, values(label_ex) gen significant = upper<0 | lower>0 preserve keep if counter>18 twoway (rcap upper lower counter, horizontal) (scatter counter b if significant==1 , color(red) msymbol(d)) (scatter counter b if significant==0, color(edkblue) msymbol(dh)), ytitle("") legend(off) xline(0) ylabel( 20 21 22 23 24 25 26 27 28 30 31 32 33 34 36 37 38 39 40 41, valuelabel tlcolor(none) ) ysize(5) yline( 4 12 29 35) graph export "D:/下载/129581-V1/Kit/results/FigC3a.pdf", replace restore preserve keep if counter<=18 twoway (rcap upper lower counter, horizontal) (scatter counter b if significant==1 , color(red) msymbol(d)) (scatter counter b if significant==0, color(edkblue) msymbol(dh)), ytitle("") legend(off) xline(0) ylabel(1 2 3 5 6 7 8 9 10 11 13 14 15 16 17 18 , valuelabel tlcolor(none) ) ysize(5) yline( 4 12 29 35) graph export "D:/下载/129581-V1/Kit/results/FigC3b.pdf", replace restore
*完整版数据和代码可以直接前往社群下载。 关于多期DID或交叠DID: 1.DID相关前沿问题“政策交错执行+堆叠DID+事件研究”, 附完整slides，2.交错(渐进)DID中, 用TWFE估计处理效应的问题, 及Bacon分解识别估计偏误，3.典范! 这篇AER在一图表里用了所有DID最新进展方法, 审稿人直接服了！4.最新Sun和Abraham(2020)和TWFE估计多期或交错DID并绘图展示结果！详细解读code！5.多期DID或渐进DID或交叠DID, 最新Stata执行命令整理如下供大家学习，6.多期DID前沿方法大讨论, e.g., 进入-退出型DID, 异质性和动态性处理效应DID, 基期选择问题等，7.交叠DID中平行趋势检验, 事件研究图绘制, 安慰剂检验的保姆级程序指南！8.欣慰! 营养午餐计划终于登上TOP5! 交叠DID+异质性稳健DID！9.用事件研究法开展政策评估的过程, 手把手教学文章！10.从双重差分法到事件研究法, 双重差分滥用与需要注意的问题，11.系统梳理DID最新进展: 从多期DID的潜在问题到当前主流解决方法和代码! 12.标准DID中的平行趋势检验,动态效应, 安慰剂检验, 预期效应教程，13.DID从经典到前沿方法的保姆级教程, 释放最完整数据和代码！
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