search for: brockwell

Hi, The default value for lag.max in ACF implementation is 10*log10(N) There several publications recommending setting lag.max to: - N/4 (Box and Jenkins, 1970; Chatfield, 1975; Anderson, 1976; Pankratz, 1983; Davis, 1986; etc.) - sqrt(N)+10 (Cryer, 1986) - 20<=N<=40 (Brockwell and Davis) Why R uses 10*log10(N) as a default? Please, give me a reference to a book or article where the recommendation for using lag.max=10*log10(N) is proposed and explained. Thanks Rafal

Hello, I am trying to collect several global measures or statistics for time-series as well as packages of R that can compute them. I have found several of them in papers and books, but the literature is so big i am sure i am missing several of them. skewness kurtosis min max mean SD trend seasonality periodicity chaos (Lyapunov Exponent) / Largest Lyapunov Exponent (i think is the same

Hi, I was wondering whether a package that can perform dynamic linear models on times series data was available for R? Many Thanks, Gavin Simpson %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Gavin Simpson [T] +44 (0)20 7679 5522 ENSIS Research Fellow [F] +44 (0)20 7679 7565 ENSIS Ltd. & ECRC [E] gavin.simpson at

...=2,plot=F) Autocorrelations of series 'X', by lag -2 -1 0 1 2 -0.139 0.593 -0.377 -0.382 0.116 > sum(x[1:9]*y[2:10])/sqrt(sum(x^2)*sum(y^2)) [1] 0.5930216 > sum(x[2:10]*y[1:9])/sqrt(sum(x^2)*sum(y^2)) [1] -0.3822885 from a quick survey on textbooks (Brockwell and Davis, Chatfield, Fuller) it looks like different authors use different conventions so that I think that it would be nice to clarify this in the documentation. Ciao Simone > R.version _ platform i386-pc-mingw32 arch i386 os mingw32 system...

Could anyone tell me where can I find some examples of the applications to economics of a Markov chain? Many thanks in advance. Luis Rivera. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not

Hi, I've seen that handle missing values makes difficult to manage data series within R. I've look at Shumway's program (ASTSA) and Brockwell & Davis pogram (ITSM), it seems that both handle NA's values without problem, or maybe, I'm getting an spurious results? My problem is with two variables: rain and river flow: the first has 7 NA values (is to say, seven months, non contiguous) and the later, 12 contiguous NA values (h...

Hello Michael, see as an online resource: http://www.statsoft.com/textbook/sttimser.html or get hold on a time series analysis textbook, like one of the monographies written by Hamilton; Luetkepohl; Brockwell & Davis; Harvey or Box & Jenkins, to name but a few. In a nutshell, PACF 'eliminates' intermediate autocorrelations compared to ACF, e.g. an AR(1) process will ordinarily have a slowly decaying ACF and a single spike in the PACF at lag 1. Both are utilised in the process of order d...

Hi All R Experts, I met with below mentioned statistics in paper "Stock Index Volatility Forecasting with High Frequency Data" by Eugenie Hol, Siem Jan Koopman http://ideas.repec.org/p/dgr/uvatin/20020068.html I would like to ask that what is "Box-Ljung portmantacau statistic based on N squared autocorrelation" ? Is it same as "Box-Ljung Statistics" of stats

Dear R users, According Brockwell & Davis (1991, Section 9.3, p.304), the penalty term for computing the AIC criteria is "p+q+1" in the context of a zero-mean ARMA(p,q) time series model. They arrived at this criterion (with this particular penalty term) estimating the Kullback-Leibler discrepancy index. In practice,...

Hi, could anyone tell me how predict() predicts the new value(s), of a MA(1) arima-modell. its really easy to make it with an AR(1), knowing the last term, but how can i or R know the last error? It would also help if somebody could tell me how to find the "open" source of the function predict(). Thanks and sorry for my poor english. -- View this message in context:

To whomever it may concern, I'm a young Industrial Engineer working on Senior Design at Georgia Tech and have found the StructTS method to be excellent for the training set for my forecasting project. There's only one problem: I don't actually understand what a Structural Time Series IS. I've looked up resources on it, and get that essentially you're dividing the Time

....max = 10) 0 1 2 3 4 5 1.000000000 0.875000000 0.625000000 0.406250000 0.250000000 0.148437500 6 7 8 9 10 0.085937500 0.048828125 0.027343750 0.015136719 0.008300781 > ## Example from Brockwell & Davis (1991, pp.92-4) > ## answer 2^(-n) * (32/3 + 8 * n) /(32/3) > n <- 1:10; 2^(-n) * (32/3 + 8 * n) /(32/3) [1] 0.875000000 0.625000000 0.406250000 0.250000000 0.148437500 0.085937500 [7] 0.048828125 0.027343750 0.015136719 0.008300781 > ARMAacf(c(1.0, -0.25), 1.0, lag.max =...

Dear list fellows, I want to study time series and use R to analyse time series of fishing data from several species (landings and cpue) investigating the correlation between them and with environmental factors (water temperature, wind, etc.). Searching at Amazon I found three books with examples in R: Time Series Analysis: With Applications in R by Jonathan D. Cryer and Jonathan D. Cryer

...is a real bug in getQ0 or if this is due to some numerical instability. However, I tried to replace getQ0 in two ways. The first one is to compute first the covariance matrix of (X_{t-1},...,X_{t-p},Z_t,...,Z_{t-q}) and this is achieved through the method of difference equations (page 93 of Brockwell and Davis). This way was apparently suggested by a referee to Gardner et al. paper (see page 314 of their paper). Q0bis <- function(phi,theta){ ## Computes the initial covariance matrix for the state space representation of Gardner et al. p <- length(phi) q <- length(theta)...

...ons" to > model > the dynmaic relationships - I think ARIMA in R should allow you to > do that > using an "x" input function. > > My own personal favourite TS book is which is a bit mathematical (i.e. > easier than the "old testament" bible - TSA by Brockwell and Davis) > is: > Time Series Analyis: Univariate and Multivariate Methods, by W. S. > Wei, > 1990. > older, but still top notch in my opinion. > > Hope this helps! > > Gerard > > > > > Antonio Olinto > <aolinto_r@bign...