> summary(lm(apply.quarterly(SP5[,4][k2k],mean) ~ eps_year_xts[k2k]+apply.quarterly(PA[k2k],mean)+apply.quarterly(CS[k2k],mean)+apply.quarterly(UC[k2k],mean)))
Call:
lm(formula = apply.quarterly(SP5[, 4][k2k], mean) ~ eps_year_xts[k2k] +
apply.quarterly(PA[k2k], mean) + apply.quarterly(CS[k2k],
mean) + apply.quarterly(UC[k2k], mean))
Residuals:
Min 1Q Median 3Q Max
-178.63 -65.62 14.98 57.55 320.86
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.791e+03 4.347e+02 -20.222 < 2e-16 ***
eps_year_xts[k2k] 7.243e+00 6.983e-01 10.373 1.01e-15 ***
apply.quarterly(PA[k2k], mean) 7.740e-02 3.649e-03 21.210 < 2e-16 ***
apply.quarterly(CS[k2k], mean) -4.994e+00 5.692e-01 -8.774 7.71e-13 ***
apply.quarterly(UC[k2k], mean) 1.304e-01 5.309e-02 2.456 0.0166 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 97.83 on 69 degrees of freedom
Multiple R-squared: 0.96, Adjusted R-squared: 0.9577
F-statistic: 413.8 on 4 and 69 DF, p-value: < 2.2e-16
tmp <- predict(lm(apply.quarterly(SP5[,4][k2k],mean) ~ eps_year_xts[k2k]+apply.quarterly(PA[k2k],mean)+apply.quarterly(CS[k2k],mean)+apply.quarterly(UC[k2k],mean)))
GSPC.predict <- merge(GSPC.predict[,-8],last(spline(seq(1,74,1),tmp,n=220)$y,n=138),suffixes = c("","eps"))
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