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Edition 4 Errata

aides who called tRump a moron ... now let's go


Chapter 1

  • it's perfect

Chapter 2

Eq (2.38)-(2.39): To be more general, the time subscript should have been $t_i$ so the equations would read $$m_t = \sum_{i=1}^n w_i(t) x_{t_i} \tag{2.38}$$ where

$$~~~~~~~~~~~~~~~~~~~~~~~~w_i(t)= K\bigl(\tfrac{t-{t_i}}{b}\bigr) \Bigm/ \sum_{j=1}^n K\bigl(\tfrac{t-{t_j}}{b}\bigr) \tag{2.39}$$

... and typically $K(z)=\exp(-z^2/2)$ is used (no need for constants because the weights are normalized).

So these two are the same:

par(mfrow=c(2,1))
tsplot(soi)      # monthly data; frequency=12 and t = 1/12, 2/12, ...
lines(ksmooth(time(soi), soi, kernel="normal", bandwidth=1), lwd=2, col=4)
# and
SOI = ts(soi, frequency=1)   # change to t = 1,2,... 
tsplot(SOI)   
lines(ksmooth(time(SOI), SOI, kernel="normal", bandwidth=12), lwd=2, col=4)

Chapter 3

  • Eq (3.10): The sum should be to $k$ (and not $k-1$): $~x_t = \phi^{-k} x_{t+k} - \sum_{j=1}^{k} \phi^{-j} w_{t+j},.$

Chapter 4

  • as if

Chapter 5

  • Example 5.1: I put this note on the R code page, but I thought I'd repeat it here. In Example 5.1, we used fracdiff, but it's not a very good package. We should have used another package such as arfima, but unfortunately it didn't make it into the revision. This is changed in Edition 5:
library(arfima)
summary(varve.fd <- arfima(log(varve)))  # d.hat = 0.3728, se(d,hat) = 0.0273
# residual stuff
innov = resid(varve.fd)  
plot.ts(innov[[1]])  
acf(innov[[1]])

Chapter 6

  • Property 6.7, equation (6.137): Left off the conditioning arguments ... the $\pi_j(t)$ in the numerator and in the denominator should be $\pi_j(t \mid t-1)$ .

  • Example 6.13: There is a correction in the code for this example. The correction has been made here textRcode. Under Example 6.13 code, the correction is for bootstrapping (lines 60-63) and the results are a little different (but the discussion is still correct):

     for (j in k){               # this is line 60
   K  = (phi*Pp[j-1]*z[j-1])/sig[j-1]  
  xp.star[j] = phi*xp.star[j-1] + Ups + K*sqrt(sig[j-1])*e.star[j-1]
  }

Chapter 7

  • not enough people read this chapter to find the bloopers ... but we're fairly certain there are a few

Elsewhere