I ran the following regression using R.
libor <- ts(diff(Libor))
ois <- ts(diff(OIS))
x <- ts(diff(Repo-OIS))
vix <- ts(diff(VIX))
cds <- ts(diff(CDS))
treasury <- ts(diff(log(P_treasury)))
mbs <- ts(diff(log(P_MBS)))
rrp <- ts(diff(log(RRP)))
axx <- ts.intersect(mbs, treasury, rrp, cds, libor, ois, x, vix)
reg3 <- lm(libor~ois+x+vix+cds+treasury+mbs+rrp, data=axx, subset=418:521)
which gave me the following output:
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.6279e-04 8.0537e-04 1.0713 0.28675
ois 1.1427e-01 5.5089e-02 2.0742 0.04076 *
x -1.0914e-02 2.0758e-02 -0.5258 0.60028
vix -1.3155e-04 1.8298e-04 -0.7190 0.47394
cds 7.9692e-05 1.0590e-04 0.7525 0.45358
treasury -3.3914e-01 1.9171e-01 -1.7690 0.08010 .
mbs -4.0022e-03 1.3883e-02 -0.2883 0.77376
rrp 1.6299e-05 1.5772e-04 0.1033 0.91791
While the variables libor, ois, x ,vix are in percent the variables treasury, mbs and rrp are in log and in million of USD. So it is a lin- log model. The variable treasury increased during the analysed time period by 47% (from 1671382 mio USD to 2461389 mio USD.). Now I want to calculate the impact of the variable treasury on my dependent variable (libor). following my book this is done: Δy=(β1/100)%Δx --> = (-0.33914/100)*47=0.1593958. So I can say, that if the variable treasury is increased by 47% my dependent variable will decline by 0.1593958% or 15.94 basispoints. Is this interpretation correct?
My question arises because in a working paper they calculated (B1*100*47). I couldn't figure out why they multiply their B1 with 100 instead of divide it by 100. Any ideas? Many thanks!
liboris a percent value, you shouldn't use ordinary least squares regression. You should use a GLM. $\endgroup$