I have a monthly dataset (6 dependent variables and 5 independent variables) for 23 countries and I need to estimate the coefficients for each country and want to store the coefficients from each regression in a .csv file. I can do it country by country but looking for something that make it quicker as I have to run regressions for different models (like CAPM, FF3 etc.) for each country. I appreciate if I can get some guidance on this. I am using the below code to do the analysis for each country.
Code:
newey2 P1 if COUNTRY_ID==1, lag(1) force est store t1 newey2 P2 if COUNTRY_ID==1, lag(1) force est store t2 newey2 P3 if COUNTRY_ID==1, lag(1) force est store t3 newey2 P4 if COUNTRY_ID==1, lag(1) force est store t4 newey2 P5 if COUNTRY_ID==1, lag(1) force est store t5 newey2 DIFF if COUNTRY_ID==1, lag(1) force est store t6 esttab t1 t2 t3 t4 t5 t6 using table_1.csv, replace compress nogaps /**CAPM**/ newey2 P1 EX_MKT if COUNTRY_ID==1, lag(1) force est store t1 newey2 P2 EX_MKT if COUNTRY_ID==1 , lag(1) force est store t2 newey2 P3 EX_MKT if COUNTRY_ID==1, lag(1) force est store t3 newey2 P4 EX_MKT if COUNTRY_ID==1, lag(1) force est store t4 newey2 P5 EX_MKT if COUNTRY_ID==1, lag(1) force est store t5 newey2 DIFF EX_MKT if COUNTRY_ID==1, lag(1) force est store t6 esttab t1 t2 t3 t4 t5 t6 using table_1.csv,append compress nogaps
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input byte COUNTRY_ID int YEAR byte MONTH double(P1 P2 P3 P4 P5 diff smb hml wml mkt_ex cma) 1 2019 3 .0046125873465531 .00862875632323654 .0414496128208215 .00648962358578197 .00921014654825113 -.0079894971836139 -.00654232826762831 .00914056808082754 .005944282428163 .002449859 .0417429304601078 1 2019 4 -.0200811201597907 -.000570887107893204 -.00841577172282152 -.00313611888553171 -.00463330975736689 -.00749475810364832 .00693676137211818 .00284333493766637 -.024026936230836 .013503434 -.0161460729988759 1 2019 5 .0193912527863333 .00626808138836978 .016337365594267 .00155661727199572 -.00327927108100917 .00379300828156456 -.00355377995639226 .00331036007041749 .00545194559329726 .000306771 .0217593647467235 1 2019 6 .023075555127377 -.00722618595870524 .0478838546476861 .0272504490307359 .0111945955358725 .00610351243332188 -.0354433669548342 -.012749908486037 -.0153574298199428 .043778207 .00937689193405734 1 2019 7 -.0169197893045661 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-.000766399691220458 .00869111318343257 .00263327818748964 .0408415830537881 .00760910703485326 -.0126113203333108 -.058954468 .0325244174979799 1 2020 10 .00901833305087965 -.00150942416670501 .0176322961664969 -.00391343833740999 .00335578425668693 .00359545632952784 -.0135883142289126 -.00453593629299935 -.00589249656010002 .002979384 -.0155436776247775 1 2020 11 .00129783413919238 .0880357068382909 -.112014820758817 -.0611762629572166 -.0920982644745734 -.0179737625284729 .0383450933465859 .0077328074923325 .0589354132768446 .149438187 -.16439540932944 1 2020 12 -.0109265181049099 -.031997379262703 .0590285424625903 .00519642490950143 .0247344002193503 -.001365600938225 -.00344483145436179 .0104779976984649 .00762702002607303 .06873572 .0240528331624842 1 2021 1 -.00327579842391743 -.00825850595839482 .0212076536882929 -.0172069062766411 -.0076782823413823 .00998408826802101 .00935732743881033 -.00907831003161402 -.0141811106614053 -.001291551 -.00952756784424498 1 2021 2 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4 .00636110696269143 -.00772235693195447 .0118076114481896 -.00249358085121237 -.00780371747273963 -.000347200545467432 -.00897402550553793 -.0019728280268375 .000537005128378984 .028289718 -.0237053309179574 2 2017 5 .0527828621589939 -.0192543365629583 -.00956903245617221 .0128562545841009 -.00157412969088472 -.0175669216081075 -.0187014607841635 -.0122591083498021 .00167579050291723 .052400294 .0391309009894046 2 2017 6 -.00537580326984195 .0149208329758294 .00238477905571138 -.013460403221149 -.0103278395978685 .00229891348276803 .0258989004624805 -.00633658195076446 .0123853869828851 -.028431494 .0122017617948463 2 2017 7 .00303646695946453 -.00338698064276573 -.0195011195309759 .00240756068284178 -.0178123705373806 -.00900060082022819 -.021774265650063 .00310784509832104 .0102217922402523 .075011852 -.0493662374482658 2 2017 8 .000449298806724048 .00715733686560427 .0255420315763589 -.000604026971644321 .00197083570573442 .0013334261870797 -.000444688353957691 .00189728689011933 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-.008205775 .064177818913341 3 2012 11 -.0184335858625224 .00553242284342465 .027153233650629 .0202854325481303 .0409941240668395 .00514212227076893 -.0158861306570509 .00565265987348573 -.0170274843604258 -.021774332 .0353474542791334 3 2012 12 .0242070540468611 -.00646789233132137 .0019757048020681 -.0199597635588768 -.0263702662560062 -.0168643383430835 -.00981307434965963 .000779737959800322 .0107908072227465 .079033034 -.0437289104901003 3 2013 1 .0244064303735581 .0407293009679382 .0129859402411091 -.0175281485610451 -.0027728925656566 -.000966297766865993 -.0018631683986121 .0112172873881741 .0208360783370859 .038416158 .0112332550999411 3 2013 2 -.0316444078787876 .00269897677135394 .0207933126467987 .0156158347450578 .0222645309342416 -.0102921391427939 .00519501938369044 -.00779203458410698 .00940848924337586 .010881336 .044847895944362 3 2013 3 -.0165153804771923 .0123969784575241 .0325007823106221 .0230486292676657 .0201758391973795 .000766271167263099 -.00395042266273161 -.00473281646794982 .0184450682660948 -.024152854 .0347633464231942 3 2013 4 -.0210077157097835 -.0149698418464625 .00932793299192547 .0247496551913774 .0436041122572965 .00390933027230879 -.0219525489365572 .0173516534480735 -.00372212791545241 -.004030647 .080611654378334 3 2013 5 -.00138159363403128 .0163585411408407 .0209908305155756 .00500299645751991 .0132149348765664 .000558459887366172 -.00476732186271116 -.00530690455742854 -.0111382018064492 -.050344521 .0118378077959061 3 2013 6 .00636459427871938 .010701454804171 .0249130030374594 .0244769741662479 .0224324467912566 .0000284403486834524 -.0150282738077587 .00209546299581399 .00133589032401832 -.10918477 .0216347872533874 end
0 Response to Newey-West estimation for time series data for multiple countries
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