Stata Session 3. Tarjei Havnes. University of Oslo. Statistics Norway. ECON 4136, UiO, 2012
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1 Stata Session 3 Tarjei Havnes 1 ESOP and Department of Economics University of Oslo 2 Research department Statistics Norway ECON 4136, UiO, 2012 Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
2 Preparation Before we start: 1 Open StataIC 11 from kiosk.uio.no (Internet Explorer!), using your UIO user name 2 Follow the instructions on installing add-ons available on the course homepage 3 - findit estout - and install the estout-package (st0085_1) 4 Download the Wooldridge data Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
3 Learning goals 1 set up panel data: -reshape-, -merge-, -append- 2 work with panel data -xtset-, -xtdata- [, -tsset-] D. and L.-operators i. and c.-operators (-c.- only in Stata 12) 3 descriptives in panel data: -xtsum-, -xttab-, -xttrans-: decompose variation graph data 4 regression in panel data fixed effects First-difference OLS with dummy variables -xtreg, fe- random effects -xtreg, re- compare FE and RE: -hausman- IV-regression: -xtivreg- Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
4 Reshape (wide form) id sex inc80 inc81 inc You can move from wide to long reshape long inc, i(id sex) j(year) or from long to wide reshape wide inc, i(id sex) j(year) (try it with country2.dta) (long form) id year sex inc Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
5 Combining datasets vertically (append). use a. append using b (a.dta) x y (b.dta) x z (b appended to a) x y z Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
6 Combining datasets horizontally (merge). use c. sort id. merge id using d (c.dta) id y (d.dta) id x (d merged to c) id y x _merge _merge==1 observation in master only _merge==2 observation in using only _merge==3 observation in both master and using Merge requires both datasets to be sorted on the merge vars Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
7 Declaring panel data Stata has many built-in commands that can be used with panel data require that Stata knows what variables denote the panel declare using -xtset idvar timevar-. loc urlpath http :// studier / emner /sv/ oekonomi / ECON4136 /h12 / undervisningsmateriale. use urlpath / mus08psidextract.dta. describe [ output omitted ]. xtset id t panel variable : id ( strongly balanced ) time variable : t, 1 to 7 delta : 1 unit Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
8 Declaring panel data The prefix -L.- and -D.- tells Stata to consider lagged or differenced variables can be combined, e.g. -LD.- allow different lag length, e.g. -L2D.-. corr lwage D. lwage wks D.wks ( obs =3570) D. D. lwage lwage wks wks lwage D wks D corr lwage D. lwage LD. lwage ( obs =2975) D. LD. lwage lwage lwage lwage D LD Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
9 Working with panel data When working with panel data, you will find great use for some of the commands we have discussed previously -collapse-, -bysort-, -tabstat-. gen wage = exp ( lwage )/ wks. preserve. collapse ( mean ) wmean = wage (sd) wsd = wage ( p50 ) wp50 = wage, by(t). order t. cl t wmean wsd wp restore. tabstat wage, s( mean sd p50 ) by(t) Summary for variables : wage by categories of: t t mean sd p Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
10 Summary statistics: Decomposing variation To describe panel data, we are often interested in summarizing the dimensions separately 1 Overall variance: s 2 O = 1 NT 1 i t (x it x) 2 2 Within variance: s 2 W = 1 NT 1 i t (x it x i ) 2 = 1 NT 1 t i (x it x i + x) 2 3 Between variance: s 2 W = 1 N 1 i ( x i x) 2. xtsum id t lwage ed exp wks south Variable Mean Std. Dev. Min Max Observations id overall N = 4165 between n = 595 within T = 7 t overall N = 4165 between n = 595 within T = 7 lwage overall N = 4165 between n = 595 within T = 7 ed overall N = 4165 between n = 595 within T = 7 exp overall N = 4165 Tarjei Havnes between (University of Oslo) Stata Session n = ECON / 20
11 Summary statistics: Decomposing variation. xttab south Overall Between Within south Freq. Percent Freq. Percent Percent Total (n = 595). xttrans south, freq residence ; south ==1 residence ; south ==1 if in the if in the South area South area 0 1 Total , , ,027 1, Total 2,535 1,035 3, h Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
12 Graphing panel data It is often useful to graph separate time series plots for some or all units. xtline lwage if id <= 20. qui xtline lwage if id <=20, overlay legend ( off ) saving ( lwage, replace ). qui xtline wks if id <=20, overlay legend ( off ) saving (wks, replace ). graph combine lwage. gph wks.gph, iscale (1) Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
13 Fixed effect, first difference Remember that the FE-model can be rephrased in first-differences.. reg D.( lwage exp exp2 wks ed), vce ( cluster id) note : _delete omitted because of collinearity note : _delete omitted because of collinearity Linear regression Number of obs = 3570 F( 2, 594) = Prob > F = R- squared = Root MSE = ( Std. Err. adjusted for 595 clusters in id) Robust D. lwage Coef. Std. Err. t P > t [95% Conf. Interval ] exp D1. ( omitted ) exp2 D wks D ed D1. ( omitted ) _cons est sto fd Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
14 Fixed effect, dummy variables Or, the FE-model can be phrased with individual dummy variables. xi: reg lwage exp exp2 wks ed i.id, vce ( cluster id) i.id _Iid_1-595 ( naturally coded ; _Iid_1 omitted ) note : _Iid_468 omitted because of collinearity Linear regression Number of obs = 4165 F( 2, 594) =. Prob > F =. R- squared = Root MSE =.1522 ( Std. Err. adjusted for 595 clusters in id) Robust lwage Coef. Std. Err. t P > t [95% Conf. Interval ] exp exp wks ed _Iid_ _Iid_ _Iid_ [ output omitted ]. est sto dum // you could also generate dummies actively : -qui tab id, gen ( iddum )- // in Stata 12, you should avoid -xi:- in favor of factor variables Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
15 Fixed effect -xtreg- You should always estimate FE-models using -xtreg- (except specification searching). xtreg lwage exp exp2 wks ed, i(id) fe note : ed omitted because of collinearity Fixed - effects ( within ) regression Number of obs = 4165 Group variable : id Number of groups = 595 R-sq: within = Obs per group : min = 7 between = avg = 7.0 overall = max = 7 F (3,3567) = corr (u_i, Xb) = Prob > F = lwage Coef. Std. Err. t P > t [95% Conf. Interval ] exp exp wks ed ( omitted ) _cons sigma_u sigma_e rho ( fraction of variance due to u_i ) F test that all u_i =0: F (594, 3567) = Prob > F = est sto fe Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
16 Random effects We can also use -xtreg, re- to estimate models with random effects, rather than fixed effects y i = ᾱ + x iβ + (α i ᾱ + u i ). xtreg lwage exp exp2 wks ed, i(id) re Random - effects GLS regression Number of obs = 4165 Group variable : id Number of groups = 595 R-sq: within = Obs per group : min = 7 between = avg = 7.0 overall = max = 7 Random effects u_i ~ Gaussian Wald chi2 (4) = corr (u_i, X) = 0 ( assumed ) Prob > chi2 = lwage Coef. Std. Err. z P > z [95% Conf. Interval ] exp exp wks ed _cons sigma_u sigma_e rho ( fraction of variance due to u_i ). est sto re Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
17 Compare RE and FE Hausman test Remember that the FE-model is consistent under the RE-model, but less efficient. But the RE-model is not consistent under the FE-model (cov (α i,x i ) 0). Test the RE-model by comparing the coefficients Hausman test.. hausman fe re y i = ᾱ + x iβ + (α i ᾱ + u i ) ---- Coefficients ---- (b) (B) (b-b) sqrt ( diag (V_b - V_B )) fe re Difference S.E. exp exp wks b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test : Ho: difference in coefficients not systematic chi2 (3) = (b-b) [( V_b - V_B )^( -1)](b-B) = Prob >chi2 = (V_b - V_B is not positive definite ). estadd scalar hausman = r( chi2 ) Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
18 IV with FE. xtivreg lwage exp exp2 ( wks = occ ), i(id) fe Fixed - effects ( within ) IV regression Number of obs = 4165 Group variable : id Number of groups = 595 R-sq: within = Obs per group : min = 7 between = avg = 7.0 overall = max = 7 Wald chi2 (3) = 6.23 e +06 corr (u_i, Xb) = Prob > chi2 = lwage Coef. Std. Err. z P > z [95% Conf. Interval ] wks exp exp _cons sigma_u sigma_e rho ( fraction of variance due to u_i ) F test that all u_i=0: F (594,3567) = Prob > F = Instrumented : wks Instruments : exp exp2 occ. est sto iv Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
19 IV with FE. xtreg wks occ exp exp2, i(id) fe Fixed - effects ( within ) regression Number of obs = 4165 Group variable : id Number of groups = 595 R-sq: within = Obs per group : min = 7 between = avg = 7.0 overall = max = 7 F (3,3567) = 7.27 corr (u_i, Xb) = Prob > F = wks Coef. Std. Err. t P > t [95% Conf. Interval ] occ exp exp _cons sigma_u sigma_e rho ( fraction of variance due to u_i ) F test that all u_i =0: F (594, 3567) = 4.19 Prob > F = est sto fs Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
20 IV with FE. esttab dum fe re iv, keep ( exp exp2 wks ed) order ( exp exp2 wks ed) stat (N hausman ) se mtit (1) (2) (3) (4) dum fe re iv exp 0.114*** 0.114*** *** 0.118*** ( ) ( ) ( ) ( ) exp *** *** *** *** ( ) ( ) ( ) ( ) wks ( ) ( ) ( ) (0.0164) ed *** *** ( ). ( ) N hausman Tarjei Havnes (University of Oslo) Stata Session 3 ECON / 20
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