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# Untitled

a guest Jul 17th, 2017 47 Never
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1. stockreturns22=data.frame(ret22=(data22)[,4],
2.                           timestamps=(data22)[,2],format="%Y/%m/%d")
3. stockreturns22.ts= xts(stockreturns22\$ret22,
4. order.by=as.Date(stockreturns22\$timestamps))
5. rt22=ts(stockreturns22\$ret22)
6. tt22=length(rt22)
7. lm22=lm(rt22[-1]~rt22[-tt22]+rt22[-tt22]:I(rt22[-tt22]<0))
8. summary(lm22)
9.
10. tauseq=c(0.02,0.1,0.2,0.5,0.8,0.9,0.98)
11. qslope22=NULL; qr22=list()
12. qqslope22=NULL;
13. for(i in 1:length(tauseq)){
14.
15. qr22[[i]]=rq(rt22[-1]~rt22[-tt22]+rt22[-tt22]:I(rt22[-tt22]
16. <0),tau=tauseq[i])
17. forecast(qr22)
18. qslope22[i]=qr22[[i]]\$coef[2]
19. qqslope22[i]=qr22[[i]]\$coef[3]
20. }
21.
22. Coefficients: (1 not defined because of singularities)
23.                                      Estimate Std. Error t value Pr(>|t|)
24.
25. (Intercept)                         504.65017   14.84839   33.99   <2e-16
26. ***
27. rt22[-tt22]                           0.34867    0.01603   21.75   <2e-16
28. ***
29. rt22[-tt22]:I(rt22[-tt22] <= 0)TRUE        NA         NA      NA       NA
30.
31. ---
32. Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
33.
34. Error in rq.fit.br(x, y, tau = tau, ...) : Singular design matrix
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