Visualisierung von Nicht-Linearität bzw. Heteroskedastizität
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1 Visualisierung von Nicht-Linearität bzw. Heteroskedastizität. use..\wooldridge\stata\wage2, clear. scatter wage IQ Kommentar: Folie 38. graph copy a3, replace. summ IQ Variable Obs Mean Std. Dev. Min Max IQ gen iqgr = autocode(iq, 8, 50, 145). table iqgr, contents(mean wage sd wage median wage iqr wage) replace iqgr mean(wage) sd(wage) med(wage) iqr(wage) replace iqgr=iqgr - 0.5*((145-50)/8) (8 real changes made). save iqgr file iqgr.dta saved Kommentar: Autocode vergibt automatisch die obere Klassengrenze für die Gruppierungsvariable. Mit diesem gen-befehl wird diese Vorgabe durch die Klassenmitte ersetzt.. list iqgr table1 table2 table3 table serrbar table1 table2 iqgr, scale(2) plot(line table1 iqgr) legend(off). graph copy a1, replace Kommentar: Die Fehlerbalken sollen nach oben und unten jeweils zwei Standardabweichungen betragen (in table2 gespeichert). 1
2 . serrbar table3 table4 iqgr, scale(.5) plot(line table3 iqgr) legend(off). graph copy a2, replace. graph combine a1 a3, xcommon rows(2) title("mittelwerte und Standardabweichung"). graph combine a2 a3, xcommon rows(2) title("median und halber Quartilsabstand") Kommentar: Die Fehlerbalken sollen nach oben und unten jeweils einen halben Quartilsabstand betragen (in table4 gespeichert). Kommentar: Folie 39 Kommentar: Folie 40. table iqgr, contents(median wage p25 wage p75 wage freq) replace iqgr med(wage) p25(wage) p75(wage) Freq graph twoway line table3 table1 table2 iqgr, legend(off). graph copy a4, replace. graph combine a4 a3, xcommon rows(2). graph box wage, over(iqgr). graph twoway mband wage IQ, bands(8) scatter wage IQ, legend(off). lowess wage IQ Kommentar: Folie 41 Kommentar: Folie 42 Kommentar: Folie 43 Kommentar: Folie 44. reg wage IQ Source SS df MS Number of obs = F( 1, 933) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = wage Coef. Std. Err. t P> t [95% Conf. Interval] IQ cons cprplot IQ Kommentar: Folie 45 2
3 . ovtest Ramsey RESET test using powers of the fitted values of wage Ho: model has no omitted variables F(3, 930) = 1.24 Prob > F = ovtest, rhs Ramsey RESET test using powers of the independent variables Ho: model has no omitted variables F(3, 930) = 1.24 Prob > F = hettest Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of wage. reg lwage IQ chi2(1) = Prob > chi2 = Source SS df MS Number of obs = F( 1, 933) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = IQ cons ovtest Ramsey RESET test using powers of the fitted values of lwage Ho: model has no omitted variables F(3, 930) = 1.16 Prob > F = hettest Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of lwage chi2(1) = 0.20 Prob > chi2 =
4 Fallstudie 1: Determinanten von Erwerbseinkommen tab year 78 or 85 Freq. Percent Cum Total 1, bysort year: reg lwage educ -> year = 78 Source SS df MS Number of obs = F( 1, 548) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE =.469 educ cons > year = 85 Source SS df MS Number of obs = F( 1, 532) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = educ cons
5 . reg lwage educ Source SS df MS Number of obs = F( 1, 1082) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = educ cons reg lwage educ y85 y85educ Source SS df MS Number of obs = F( 3, 1080) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = educ y y85educ cons test y85 y85educ ( 1) y85 = 0 ( 2) y85educ = 0 F( 2, 1080) = Prob > F = test y85educ ( 1) y85educ = 0 F( 1, 1080) = 5.28 Prob > F = display sqrt(5.28)
6 Fallstudie 2: Analyse quasi-experimenteller Daten. use..\wooldridge\stata\kielmc. bysort year: reg rprice nearinc -> year = 1981 Source SS df MS Number of obs = F( 1, 140) = Model e e+10 Prob > F = Residual e R-squared = Adj R-squared = Total e e+09 Root MSE = nearinc cons > year = 1978 Source SS df MS Number of obs = F( 1, 177) = Model e e+10 Prob > F = Residual e R-squared = Adj R-squared = Total e Root MSE = nearinc cons
7 . reg rprice y81 nearinc y81nrinc Source SS df MS Number of obs = F( 3, 317) = Model e e+10 Prob > F = Residual e R-squared = Adj R-squared = Total e e+09 Root MSE = y nearinc y81nrinc cons reg rprice y81 nearinc y81nrinc age agesq Source SS df MS Number of obs = F( 5, 315) = Model e e+10 Prob > F = Residual e R-squared = Adj R-squared = Total e e+09 Root MSE = y nearinc y81nrinc age agesq cons
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