1 IV Loop over RHS

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Regression with a Variety of Outcome Variables and Right Hand Side Variables. There are M outcome variables, and there are N alternative right hand side variables. Regress each M outcome variable and each N alternative right hand side variable, with some common sets of controls and perhaps shared instruments. The output file is a M by N matrix of coefficients, with proper variable names and row names. The matrix stores coefficients for this key endogenous variable.

Dependency: R4Econ/linreg/ivreg/ivregdfrow.R

1.1 Construct Program

The program relies on double lapply. lapply is used for convenience, not speed.

ff_reg_mbyn <- function(list.vars.y, list.vars.x,
                        vars.c, vars.z, df,
                        return_all = FALSE,
                        stats_ends = 'value', time = FALSE) {

  # regf.iv() function is from C:\Users\fan\R4Econ\linreg\ivreg\ivregdfrow.R
  if (time) {
    start_time <- Sys.time()
  }

  if (return_all) {
    df.reg.out.all <-
      bind_rows(lapply(list.vars.x,
                       function(x) (
                         bind_rows(
                           lapply(list.vars.y, regf.iv,
                                  vars.x=x, vars.c=vars.c, vars.z=vars.z, df=df))
                       )))

  } else {
    df.reg.out.all <-
      (lapply(list.vars.x,
              function(x) (
                bind_rows(
                  lapply(list.vars.y, regf.iv,
                         vars.x=x, vars.c=vars.c, vars.z=vars.z, df=df)) %>%
                  select(vars_var.y, starts_with(x)) %>%
                  select(vars_var.y, ends_with(stats_ends))
              ))) %>% reduce(full_join)
  }

  if (time) {
    end_time <- Sys.time()
    print(paste0('Estimation for all ys and xs took (seconds):',
                 end_time - start_time))
  }

  return(df.reg.out.all)
}

1.2 Prepare Data

# Library
library(tidyverse)
library(AER)

# Load Sample Data
setwd('C:/Users/fan/R4Econ/_data/')
df <- read_csv('height_weight.csv')

# Source Dependency
source('C:/Users/fan/R4Econ/linreg/ivreg/ivregdfrow.R')

# Setting
options(repr.matrix.max.rows=50, repr.matrix.max.cols=50)

Parameters.

var.y1 <- c('hgt')
var.y2 <- c('wgt')
var.y3 <- c('vil.id')
list.vars.y <- c(var.y1, var.y2, var.y3)

var.x1 <- c('prot')
var.x2 <- c('cal')
var.x3 <- c('wealthIdx')
var.x4 <- c('p.A.prot')
var.x5 <- c('p.A.nProt')
list.vars.x <- c(var.x1, var.x2, var.x3, var.x4, var.x5)

vars.z <- c('indi.id')
vars.c <- c('sex', 'wgt0', 'hgt0', 'svymthRound')

1.3 Program Testing

1.3.1 Test Program OLS Z-Stat

vars.z <- NULL
suppressWarnings(suppressMessages(
  ff_reg_mbyn(list.vars.y, list.vars.x,
              vars.c, vars.z, df,
              return_all = FALSE,
              stats_ends = 'value'))) %>%
  kable() %>%
  kable_styling_fc_wide()

vars_var.y	prot_tvalue	cal_tvalue	wealthIdx_tvalue	p.A.prot_tvalue	p.A.nProt_tvalue
hgt	18.8756010031786	23.4421863484661	13.508899618216	3.83682180045518	32.5448257554855
wgt	16.3591125056062	17.3686031309332	14.1390521528113	1.36958319982295	12.0961557911467
vil.id	-14.9385580468907	-19.6150110809452	34.0972558327347	8.45943342783186	17.7801422421419

1.3.2 Test Program IV T-stat

vars.z <- c('indi.id')
suppressWarnings(suppressMessages(
  ff_reg_mbyn(list.vars.y, list.vars.x,
              vars.c, vars.z, df,
              return_all = FALSE,
              stats_ends = 'value'))) %>%
  kable() %>%
  kable_styling_fc_wide()

vars_var.y	prot_zvalue	cal_zvalue	wealthIdx_zvalue	p.A.prot_zvalue	p.A.nProt_zvalue
hgt	8.87674929300964	12.0739764947235	4.62589553677969	26.6373587567312	32.1162192385744
wgt	5.60385871756365	6.1225187008946	5.17869536991717	11.9295584469998	12.3509307017263
vil.id	-9.22106223347162	-13.0586007975839	-51.5866689219593	-29.9627476577329	-38.3528894620707

1.3.3 Test Program OLS Coefficient

vars.z <- NULL
suppressWarnings(suppressMessages(
  ff_reg_mbyn(list.vars.y, list.vars.x,
              vars.c, vars.z, df,
              return_all = FALSE,
              stats_ends = 'Estimate'))) %>%
  kable() %>%
  kable_styling_fc_wide()

vars_var.y	prot_Estimate	cal_Estimate	wealthIdx_Estimate	p.A.prot_Estimate	p.A.nProt_Estimate
hgt	0.049431093806755	0.00243408846205622	0.21045655488185	3.86952250259526e-05	0.00542428867316449
wgt	16.5557424523585	0.699072500364623	106.678721085969	0.00521731297924587	0.779514232050632
vil.id	-0.0758835879205584	-0.00395676177098486	0.451733304543324	0.000149388430455142	0.00526237555581024

1.3.4 Test Program IV coefficient

vars.z <- c('indi.id')
suppressWarnings(suppressMessages(
  ff_reg_mbyn(list.vars.y, list.vars.x,
              vars.c, vars.z, df,
              return_all = FALSE,
              stats_ends = 'Estimate'))) %>%
  kable() %>%
  kable_styling_fc_wide()

vars_var.y	prot_Estimate	cal_Estimate	wealthIdx_Estimate	p.A.prot_Estimate	p.A.nProt_Estimate
hgt	0.859205733632614	0.0238724384575419	0.144503490136948	0.00148073028434642	0.0141317656200726
wgt	98.9428234201406	2.71948246216953	69.1816142883022	0.221916473012486	2.11856940494335
vil.id	-6.02451379136132	-0.168054407187466	-1.91414470908345	-0.00520794333267238	-0.0494468877742109

1.3.5 Test Program OLS Return All

vars.z <- NULL
t(suppressWarnings(suppressMessages(
  ff_reg_mbyn(list.vars.y, list.vars.x,
            vars.c, vars.z, df,
            return_all = TRUE,
            stats_ends = 'Estimate')))) %>%
  kable() %>%
  kable_styling_fc_wide()

X.Intercept._Estimate	27.3528514188608	99.873884728925	31.4646660224049	27.9038445914729	219.626705179399	30.5103987898551	35.7840188807906	-2662.74787734003	29.2381039651127	23.9948407749744	-547.959546430028	22.3367814226238	24.4904444950827	-476.703973630552	22.7781908464511
X.Intercept._Pr…t..	5.68247182214952e-231	0.75529705553815	6.78164655340399e-84	8.24252673989353e-242	0.493216914827181	1.62608789535248e-79	2.26726906489443e-145	7.13318862990131e-05	1.53578035267873e-124	2.11912344053336e-165	0.0941551350855875	3.04337266226599e-49	2.34941965806705e-181	0.143844033032183	9.58029450711211e-52
X.Intercept._Std.Error	0.831272666092284	320.450650378664	1.61328519718754	0.828072565159449	320.522532223672	1.60831193651104	1.38461348429899	670.301542938561	1.22602177264147	0.86658104216672	327.343126852912	1.5098937308759	0.843371070670838	326.132837036936	1.5004526558957
X.Intercept._tvalue	32.9047886867776	0.31166697465244	19.503474077155	33.6973421962119	0.685214557790078	18.9704485163756	25.8440491058106	-3.97246270039407	23.8479483950102	27.6890903532576	-1.6739607509042	14.7936116071335	29.0387533397398	-1.46168652614567	15.1808794212527
adj.r.squared_v	0.814249026159781	0.60716936506893	0.0373247512680971	0.81608922805658	0.607863678511207	0.0453498711076042	0.935014931990565	0.92193683733695	0.059543122812776	0.814690803458616	0.617300597776144	0.0261131074199838	0.824542352656376	0.620250730454724	0.0385437355117917
df1_v	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6
df2_v	18957	18962	18999	18957	18962	18999	25092	25102	30013	18587	18591	18845	18587	18591	18845
df3_v	6	6	6	6	6	6	6	6	6	6	6	6	6	6	6
hgt0_Estimate	0.60391817340617	56.3852027199184	-0.296844389234445	0.589847843438394	52.9707041800704	-0.273219210757899	0.439374451256039	47.176969664749	-0.35908163982046	0.687269209411865	72.105560623359	-0.108789161111504	0.622395388389206	62.7336220289257	-0.157811627494693
hgt0_Pr…t..	1.14533314566771e-183	1.52417506966835e-12	1.40290395213743e-13	7.79174951119325e-177	3.05720143843395e-11	8.49149153665126e-12	2.71000479249152e-36	0.00520266507060071	2.41020063623865e-31	1.31914432912869e-220	4.78613024244006e-19	0.0034801146146182	1.11511327164938e-190	8.38546282719268e-15	2.13723119924676e-05
hgt0_Std.Error	0.0206657538633713	7.96735224000553	0.0401060913799595	0.0205836398278421	7.96822145797115	0.0399777363511633	0.0348701896610764	16.8823489375743	0.0307984635553859	0.0213841849324282	8.07744906400683	0.0372288594891345	0.0208846437570215	8.07589192978212	0.0371223237183417
hgt0_tvalue	29.2231378249683	7.0770314931977	-7.40147890309685	28.6561486875877	6.64774497790599	-6.83428417151858	12.6002885423502	2.79445531182864	-11.659076407325	32.1391351404584	8.92677379355593	-2.92217281443323	29.8015803204665	7.76801157994423	-4.25112470577158
prot_Estimate	0.049431093806755	16.5557424523585	-0.0758835879205584	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
prot_Pr…t..	9.54769322304645e-79	9.61203373222183e-60	3.56396093562335e-50	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
prot_Std.Error	0.00261878251179557	1.01201959743751	0.00507971302734622	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
prot_tvalue	18.8756010031786	16.3591125056062	-14.9385580468907	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
r.squared_v	0.814298005954592	0.607272921412825	0.0375780335372857	0.816137722617266	0.60796705182314	0.0456010419476623	0.93502787877066	0.921952383432195	0.0596997716363463	0.814740639193486	0.617403496088206	0.0263714328556815	0.824589538985803	0.620352835549783	0.0387987636986586
sexMale_Estimate	0.935177182449406	415.163616765357	-0.254089999175318	0.893484662055608	405.534891838028	-0.181389489610951	1.80682463132073	999.926876716707	-0.33436777751525	0.932686930233136	397.141948675354	-0.445232370681998	0.96466980500711	401.59056368102	-0.423829627017582
sexMale_Pr…t..	2.36432111724607e-51	2.48252880290814e-67	0.0343768259467621	2.08765935335877e-47	2.51355675686752e-64	0.129768754080748	1.26527362032354e-66	2.64630894140004e-86	0.000311174554787706	7.90489020586094e-47	6.19449742677662e-59	7.93666802281971e-05	1.24556615236597e-52	1.18469030741261e-60	0.00015644693636154
sexMale_Std.Error	0.0618482294097262	23.8518341439675	0.120093045309631	0.0616078355613525	23.8567507583516	0.11972270545355	0.104475287357902	50.5879876531386	0.0927193334338799	0.0647209948973267	24.4473730956481	0.112797805327952	0.0629827627260302	24.3549086073387	0.112083516545945
sexMale_tvalue	15.1205166481668	17.4059409544552	-2.11577613441484	14.5027763743757	16.9987478993157	-1.51508010885476	17.2942776901016	19.7660931597596	-3.60623577771614	14.4108867873979	16.2447698213453	-3.94717228218682	15.316409812052	16.4891016491029	-3.78137339083082
sigma_v	4.21029844914315	1623.77111076428	8.18491760066961	4.18939119979502	1622.33549880859	8.15073036560541	8.18607049768594	3964.45339913597	7.93450742809862	4.35662621773428	1645.77655955938	7.6435668370875	4.23923961592693	1639.42085007515	7.59462918474114
svymthRound_Estimate	0.87166589100565	189.04290688382	-0.0154759587993917	0.851989049736817	185.318286001897	0.0201471237605442	0.432815253441723	189.877994795061	0.00215144302579706	0.91961467696139	205.597385664745	-0.0509574460702806	0.921894094780682	205.945143306004	-0.0557204455206461
svymthRound_Pr…t..	0	0	0.0397984032097113	0	0	0.0117151185126433	0	0	0.000447277200167272	0	0	1.37139389802397e-18	0	0	7.79141497751766e-23
svymthRound_Std.Error	0.00387681209575621	1.4955473831309	0.00752730297891317	0.00411253488213795	1.59266949679221	0.00799217807522278	0.000728323735328998	0.352701518968252	0.000612792699568233	0.00331108017589107	1.25083486490652	0.00578476859618168	0.00317113547025635	1.22639878616071	0.00565696328562864
svymthRound_tvalue	224.840892330022	126.403823119306	-2.05597660181154	207.168832400006	116.357025971267	2.52085521254888	594.262183761197	538.353209678558	3.51088227277012	277.738571133786	164.368128386085	-8.80889965139067	290.714194782148	167.926734460268	-9.84988636256528
vars_var.y	hgt	wgt	vil.id	hgt	wgt	vil.id	hgt	wgt	vil.id	hgt	wgt	vil.id	hgt	wgt	vil.id
vars_vars.c	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound
vars_vars.x	prot	prot	prot	cal	cal	cal	wealthIdx	wealthIdx	wealthIdx	p.A.prot	p.A.prot	p.A.prot	p.A.nProt	p.A.nProt	p.A.nProt
wgt0_Estimate	-0.000146104685986986	0.637023553461055	-0.000903390591533867	-0.000116898230009949	0.649394003614758	-0.000941137072743919	0.00122231975126219	1.32870822160235	-0.000845938526704796	-0.000489534836079617	0.580023505722658	-0.00156196911156061	3.23596154259101e-05	0.65551206304675	-0.00115432723977403
wgt0_Pr…t..	0.136011583497549	2.96480083692757e-63	2.05763549729273e-06	0.230228828649018	7.43034302413852e-66	6.66901196231733e-07	1.22269348058816e-13	6.75367630221077e-62	4.32675510884621e-09	7.77000489086602e-07	7.42419220783427e-54	1.40362012201826e-19	0.740027016459552	4.09082062947785e-67	2.75472781728448e-11
wgt0_Std.Error	9.79994437486573e-05	0.0378027371614794	0.000190221503167431	9.74307633896921e-05	0.037739875283113	0.000189270503626621	0.000164767846917989	0.0798131859486402	0.000144040382619518	9.90410500454311e-05	0.0374185042114355	0.000172365145002826	9.75208524392668e-05	0.0377202854835204	0.000173241059789276
wgt0_tvalue	-1.49087260496811	16.8512547316329	-4.74915073475531	-1.19980821193398	17.2071051836606	-4.97244448929308	7.41843614592224	16.6477281392748	-5.872926128913	-4.94274682926991	15.5009805428138	-9.0619777654873	0.331822524275644	17.3782370584956	-6.66312732777158
cal_Estimate	NA	NA	NA	0.00243408846205622	0.699072500364623	-0.00395676177098486	NA	NA	NA	NA	NA	NA	NA	NA	NA
cal_Pr…t..	NA	NA	NA	8.01672708877986e-120	4.71331900885298e-67	7.94646124029527e-85	NA	NA	NA	NA	NA	NA	NA	NA	NA
cal_Std.Error	NA	NA	NA	0.000103833679413418	0.0402492068645167	0.000201721108117477	NA	NA	NA	NA	NA	NA	NA	NA	NA
cal_tvalue	NA	NA	NA	23.4421863484661	17.3686031309332	-19.6150110809452	NA	NA	NA	NA	NA	NA	NA	NA	NA
wealthIdx_Estimate	NA	NA	NA	NA	NA	NA	0.21045655488185	106.678721085969	0.451733304543324	NA	NA	NA	NA	NA	NA
wealthIdx_Pr…t..	NA	NA	NA	NA	NA	NA	1.93494257274268e-41	3.2548345535026e-45	4.82890644822007e-250	NA	NA	NA	NA	NA	NA
wealthIdx_Std.Error	NA	NA	NA	NA	NA	NA	0.0155791042075745	7.54496977117083	0.0132483771350785	NA	NA	NA	NA	NA	NA
wealthIdx_tvalue	NA	NA	NA	NA	NA	NA	13.508899618216	14.1390521528113	34.0972558327347	NA	NA	NA	NA	NA	NA
p.A.prot_Estimate	NA	NA	NA	NA	NA	NA	NA	NA	NA	3.86952250259526e-05	0.00521731297924587	0.000149388430455142	NA	NA	NA
p.A.prot_Pr…t..	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.000125048896903791	0.170833589209346	2.88060045451681e-17	NA	NA	NA
p.A.prot_Std.Error	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.00852286184785e-05	0.00380941660201464	1.76593895713687e-05	NA	NA	NA
p.A.prot_tvalue	NA	NA	NA	NA	NA	NA	NA	NA	NA	3.83682180045518	1.36958319982295	8.45943342783186	NA	NA	NA
p.A.nProt_Estimate	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.00542428867316449	0.779514232050632	0.00526237555581024
p.A.nProt_Pr…t..	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5.25341325077391e-226	1.47950939943836e-33	3.7685780281174e-70
p.A.nProt_Std.Error	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.000166671307872964	0.06444313759758	0.000295969260771016
p.A.nProt_tvalue	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	32.5448257554855	12.0961557911467	17.7801422421419

1.3.6 Test Program IV Return All

vars.z <- c('indi.id')
t(suppressWarnings(suppressMessages(
  ff_reg_mbyn(list.vars.y, list.vars.x,
            vars.c, vars.z, df,
            return_all = TRUE,
            stats_ends = 'Estimate')))) %>%
  kable() %>%
  kable_styling_fc_wide()

X.Intercept._Estimate	40.2173991882938	1408.1626637032	-64.490636067872	39.6732302990235	1325.54736576331	-59.8304089440729	35.5561817357046	-2791.221534909	21.8005242861645	24.3009261707644	-499.067024090554	21.4632286881661	25.299209739617	-352.278518334717	17.9359211844992
X.Intercept._Pr…z..	3.69748206920405e-59	0.00217397545504963	0.000109756271656929	1.30030240177373e-103	0.00138952700443324	3.75547414421179e-07	2.01357089467444e-142	1.95034793045284e-05	1.17899313785408e-34	1.97968607369592e-84	0.155922992163314	1.84405333738942e-09	1.29388565624566e-157	0.287184942021997	1.13855583530306e-12
X.Intercept._Std.Error	2.47963650430699	459.377029874119	16.673099250727	1.83545587849039	414.645900526211	11.7754321198995	1.39936229104453	653.605248808641	1.77547715237629	1.2481331128579	351.723712333143	3.57067054655531	0.945826571474308	330.990098562619	2.52170174723203
X.Intercept._zvalue	16.2190704639323	3.06537456626657	-3.86794531107106	21.6149190857443	3.19681772828602	-5.08095230263053	25.4088465605032	-4.27050048939585	12.2786847788984	19.4698193008609	-1.41891776582254	6.01097984491234	26.748254386829	-1.0643173915611	7.11262590993832
hgt0_Estimate	0.403139725681418	35.5765914326678	1.20995060148712	0.357976348180876	31.0172706497394	1.5037447089682	0.460434521499963	59.1545587745268	0.412512139031067	0.515794899569023	46.2591615803265	0.520812513246773	0.510868687340428	45.5654716961559	0.534362107844268
hgt0_Pr…z..	1.25009876641748e-13	0.000445802636381424	0.00097112649404847	2.82141265004339e-17	0.0013100303315764	3.70002169470828e-08	2.98739737280869e-37	0.000542570320022534	3.02226357947691e-20	8.57492956381676e-59	2.8561488738123e-07	1.10039023747789e-08	3.24936430168307e-102	6.3454545304127e-08	3.42500501176006e-17
hgt0_Std.Error	0.0543948312973965	10.1318250572006	0.366789440587685	0.0423453726223874	9.65135595900306	0.273179527952317	0.0361031059207763	17.1025823111635	0.0447499166716409	0.0319035514861838	9.01263684093548	0.0911390672920558	0.0237991645877977	8.42434865398195	0.063380058773461
hgt0_zvalue	7.41136089709158	3.51137048180512	3.29876072644971	8.45373003027063	3.21377335801252	5.50460248701607	12.7533216258548	3.45880859967647	9.21816552325528	16.1673191711084	5.13270005180026	5.71448149208973	21.4658243761363	5.40878275196011	8.4310762436216
prot_Estimate	0.859205733632614	98.9428234201406	-6.02451379136132	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
prot_Pr…z..	6.88427338202428e-19	2.09631602352917e-08	2.94171378745816e-20	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
prot_Std.Error	0.0967928354481331	17.6561952052848	0.653342710289155	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
prot_zvalue	8.87674929300964	5.60385871756365	-9.22106223347162	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Sargan_df1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
sexMale_Estimate	0.154043421788007	333.799680049259	5.41175429817609	0.106307556057668	330.452608866758	5.83118942788808	1.80283907885782	997.747599807148	-0.452827875182598	1.02741625216018	411.365911332896	-0.789122421167432	1.02009164592608	409.820707458838	-0.746032636368145
sexMale_Pr…z..	0.38807812932888	5.06413216642981e-24	5.80077629932476e-06	0.423490075745117	2.52735690930834e-27	6.12283824664132e-12	1.1689328480129e-65	2.02347084785411e-89	0.000647195788038449	1.69796551008584e-27	2.05327249429949e-54	0.00428270841484855	1.70848440093529e-51	2.36314216739034e-62	6.57521045473888e-05
sexMale_Std.Error	0.178475271469781	33.0216035385405	1.19371921154418	0.132821186086547	30.5174257711927	0.847955715223327	0.105343525210948	49.7632792630648	0.132754263303719	0.0945646985181925	26.4822313532216	0.276250047248363	0.0675715533063635	24.5920104216267	0.18692145837209
sexMale_zvalue	0.86310792817082	10.1085242471545	4.53352366774387	0.800381017440976	10.8283251459136	6.87676174970095	17.113904962338	20.0498764266063	-3.41102322376347	10.8646912458831	15.5336574870174	-2.85655126226267	15.0964658352764	16.6647907361992	-3.99115565898846
svymthRound_Estimate	0.20990165085783	121.78985943172	4.84745570027424	0.322893837128574	135.494858749214	4.07024693316581	0.433164820953121	190.07735139541	0.0137438264666969	1.00582859923509	218.549980922774	-0.369567838754916	0.929266902426869	207.078222946319	-0.0985678389223824
svymthRound_Pr…z..	0.00846239710392287	5.96047652813855e-17	2.07373887977152e-19	9.66146445882893e-11	4.48931446042076e-34	5.64723572160763e-36	0	0	1.57416908709431e-66	0	0	2.42696379701225e-102	0	0	1.84569897952709e-27
svymthRound_Std.Error	0.0797183179471441	14.5577085129475	0.538050140685815	0.0498896912188091	11.133488331472	0.325043349284718	0.00120472816008751	0.739269879490032	0.000797655931686456	0.00746867714609297	1.9315711781906	0.0172056989832505	0.00539330635998817	1.46167854745858	0.00907867488118012
svymthRound_zvalue	2.63304164291327	8.36600480930094	9.00930105527994	6.47215545416802	12.1700274626596	12.5221664806331	359.553993426746	257.11496798237	17.2302692435808	134.672925279848	113.146221785884	-21.4793853545086	172.300040161061	141.671520941705	-10.8570733297996
vars_var.y	hgt	wgt	vil.id	hgt	wgt	vil.id	hgt	wgt	vil.id	hgt	wgt	vil.id	hgt	wgt	vil.id
vars_vars.c	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound	sex+wgt0+hgt0+svymthRound
vars_vars.x	prot	prot	prot	cal	cal	cal	wealthIdx	wealthIdx	wealthIdx	p.A.prot	p.A.prot	p.A.prot	p.A.nProt	p.A.nProt	p.A.nProt
vars_vars.z	indi.id	indi.id	indi.id	indi.id	indi.id	indi.id	indi.id	indi.id	indi.id	indi.id	indi.id	indi.id	indi.id	indi.id	indi.id
Weakinstruments_df1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Weakinstruments_df2	18957	18962	18999	18957	18962	18999	25092	25102	30013	18587	18591	18845	18587	18591	18845
Weakinstruments_p.value	1.42153759923994e-19	4.45734829676713e-19	5.72345606957941e-20	1.77770827184424e-37	4.03760292920738e-37	5.47447735093002e-38	0	0	0	0	0	0	0	0	0
Weakinstruments_statistic	82.0931934821266	79.8251182827386	83.8989817367586	164.392129625299	162.747072038429	166.75260665498	7029.47383089383	7038.38467113128	12942.6315513372	1710.98122418591	1715.15052113399	1725.71954882902	5097.88462603711	5110.7741807338	5136.55662964887
wgt0_Estimate	-0.00163274724538111	0.492582112313709	0.00999798623641602	-0.000658938519302931	0.601258436431587	0.00326074237566435	0.00112485055604169	1.27282038539707	-0.00512158791392237	0.000716628918444932	0.761704518610475	-0.00601345031606092	0.000922100117259348	0.792700893714085	-0.00668277875606482
wgt0_Pr…z..	4.88365163639597e-08	2.33136555228405e-20	7.95432753711715e-07	0.00032843149807424	2.0921134733036e-48	0.00667886646012294	2.26123807446765e-11	6.67525280062144e-56	6.51923753120087e-127	2.43477572076212e-06	8.2201479288098e-69	5.19751747217521e-44	1.68237436753105e-15	4.81415543564975e-82	2.54848840100353e-105
wgt0_Std.Error	0.00029928487659495	0.0532753838702833	0.00202532507408065	0.000183457551985601	0.0411255751282477	0.00120214094164169	0.000168187467853553	0.08080475140115	0.000213715312589078	0.000152036990658929	0.0434474820359048	0.00043218241369976	0.00011580150512068	0.0413159097814445	0.000306609919182859
wgt0_zvalue	-5.45549532591606	9.24596082710666	4.93648469787221	-3.59177647456371	14.6200614716414	2.71244598924594	6.68807593334564	15.7518012657231	-23.9645341827701	4.71351685756907	17.531614789115	-13.9141485757875	7.96276452796019	19.1863351892132	-21.7957030675165
Wu.Hausman_df1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Wu.Hausman_df2	18956	18961	18998	18956	18961	18998	25091	25101	30012	18586	18590	18844	18586	18590	18844
Wu.Hausman_p.value	1.53929570343279e-118	3.13415891402799e-08	0	2.88592507054107e-108	7.6495944085204e-07	0	0.0221987672063003	0.0099360023036833	0	1.80909125272768e-238	2.14946499922491e-35	0	3.15182965429765e-108	1.7681125741529e-17	0
Wu.Hausman_statistic	543.467268879953	30.6481856102772	5652.51924792859	494.955883488045	24.4605456760994	5583.56513052781	5.23078768861684	6.6473469952822	25949.7118056025	1119.87022468742	154.793296861581	4826.92242730041	494.903094649183	72.530787010352	7607.83405438193
cal_Estimate	NA	NA	NA	0.0238724384575419	2.71948246216953	-0.168054407187466	NA	NA	NA	NA	NA	NA	NA	NA	NA
cal_Pr…z..	NA	NA	NA	1.44956616452661e-33	9.21076021290446e-10	5.67614501764414e-39	NA	NA	NA	NA	NA	NA	NA	NA	NA
cal_Std.Error	NA	NA	NA	0.00197718112735887	0.444177077282291	0.0128692506794877	NA	NA	NA	NA	NA	NA	NA	NA	NA
cal_zvalue	NA	NA	NA	12.0739764947235	6.1225187008946	-13.0586007975839	NA	NA	NA	NA	NA	NA	NA	NA	NA
wealthIdx_Estimate	NA	NA	NA	NA	NA	NA	0.144503490136948	69.1816142883022	-1.91414470908345	NA	NA	NA	NA	NA	NA
wealthIdx_Pr…z..	NA	NA	NA	NA	NA	NA	3.72983264926432e-06	2.23442991281176e-07	0	NA	NA	NA	NA	NA	NA
wealthIdx_Std.Error	NA	NA	NA	NA	NA	NA	0.0312379492766376	13.358888551386	0.0371054140359243	NA	NA	NA	NA	NA	NA
wealthIdx_zvalue	NA	NA	NA	NA	NA	NA	4.62589553677969	5.17869536991717	-51.5866689219593	NA	NA	NA	NA	NA	NA
p.A.prot_Estimate	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.00148073028434642	0.221916473012486	-0.00520794333267238	NA	NA	NA
p.A.prot_Pr…z..	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.50759287066563e-156	8.30126393398654e-33	3.00201194005694e-197	NA	NA	NA
p.A.prot_Std.Error	NA	NA	NA	NA	NA	NA	NA	NA	NA	5.55884799941827e-05	0.0186022369560791	0.000173813943639721	NA	NA	NA
p.A.prot_zvalue	NA	NA	NA	NA	NA	NA	NA	NA	NA	26.6373587567312	11.9295584469998	-29.9627476577329	NA	NA	NA
p.A.nProt_Estimate	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.0141317656200726	2.11856940494335	-0.0494468877742109
p.A.nProt_Pr…z..	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.61782083774363e-226	4.81511329043196e-35	0
p.A.nProt_Std.Error	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0.000440019589949091	0.17153115470458	0.00128926108222202
p.A.nProt_zvalue	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	32.1162192385744	12.3509307017263	-38.3528894620707

1.4 Program Line by Line

Set Up Parameters

vars.z <- c('indi.id')
vars.z <- NULL
vars.c <- c('sex', 'wgt0', 'hgt0', 'svymthRound')

1.4.1 Lapply

df.reg.out <- as_tibble(
  bind_rows(lapply(list.vars.y, regf.iv,
                   vars.x=var.x1, vars.c=vars.c, vars.z=vars.z, df=df)))

1.4.2 Nested Lapply Test

lapply(list.vars.y, function(y) (mean(df[[var.x1]], na.rm=TRUE) +
                                   mean(df[[y]], na.rm=TRUE)))

## [[1]]
## [1] 98.3272
## 
## [[2]]
## [1] 13626.51
## 
## [[3]]
## [1] 26.11226

lapplytwice <- lapply(
  list.vars.x, function(x) (
    lapply(list.vars.y, function(y) (mean(df[[x]], na.rm=TRUE) +
                                       mean(df[[y]], na.rm=TRUE)))))
# lapplytwice

1.4.3 Nested Lapply All

df.reg.out.all <- bind_rows(
  lapply(list.vars.x,
         function(x) (
           bind_rows(
             lapply(list.vars.y, regf.iv,
                    vars.x=x, vars.c=vars.c, vars.z=vars.z, df=df))
         )))

# df.reg.out.all %>%
#   kable() %>%
#   kable_styling_fc_wide()

1.4.4 Nested Lapply Select

df.reg.out.all <-
  (lapply(list.vars.x,
          function(x) (
            bind_rows(lapply(list.vars.y, regf.iv,
                             vars.x=x, vars.c=vars.c, vars.z=vars.z, df=df)) %>%
              select(vars_var.y, starts_with(x)) %>%
              select(vars_var.y, ends_with('value'))
          ))) %>% reduce(full_join)

df.reg.out.all %>%
  kable() %>%
  kable_styling_fc_wide()

vars_var.y	prot_tvalue	cal_tvalue	wealthIdx_tvalue	p.A.prot_tvalue	p.A.nProt_tvalue
hgt	18.8756010031786	23.4421863484661	13.508899618216	3.83682180045518	32.5448257554855
wgt	16.3591125056062	17.3686031309332	14.1390521528113	1.36958319982295	12.0961557911467
vil.id	-14.9385580468907	-19.6150110809452	34.0972558327347	8.45943342783186	17.7801422421419

R OLS and Instrumental Variable Regression M Outcomes and N RHS Alternatives

Fan Wang

2020-04-01