similar to: How to specify an ARMA(1, [1,4]) model? Solved

Hi, I'm trying to model an ARMA(1,[1,4]), i.e. I want only lags 1 and 4 of the Moving Average part. It's the '[1,4]' part that is giving me a problem. I've tried different arma's and arima's in different packages, namely: packages tseries, fArma, FinTS, timeSeries, TSA, Zelig, ds1, forecast For example, with package FinTS: > ( ARIMA(y, order=c(1,0,c(1,4))) )

> It might clarify your thinking to note that a seasonal ARIMA model > is just an ``ordinary'' ARIMA model with some coefficients > constrained to be 0 in an efficient way. E.g. a seasonal AR(1) s = > 4 model is the same as an ordinary (nonseasonal) AR(4) model with > coefficients theta_1, theta_2, and theta_3 constrained to be 0. You > can get the same answer as from

Hello, The "arma" function in the "tseries" package allows estimation of models with specific "ar" and "ma" lags with its "lag" argument. For example: y[t] = a[0] + a[1]y[t-3] +b[1]e[t-2] + e[t] can be estimated with the following specification : arma(y, lag=list(ar=3,ma=2)). Is this possible with the "arima" function in the

# Dear List, # I want to characterize a time series according to its Quarter components. # My data ("a.ts": http://docs.google.com/View?id=dfvvwzr2_478cr9k4cdb)? look like: #???????????????? Qtr1????????? Qtr2????????? Qtr3????????? Qtr4 #?? 1948 -0.0714961837? 0.0101747827? 0.0654816569 -0.0227830729 #?? 1949 -0.1175517556? 0.1151378692? 0.1015777858 -0.1971535900 #?? 1950?

Hello all, I have been using R's time series capabilities to perform analysis for quite some time now and I am having some questions regarding its reliability. In several cases I have had substantial disagreement between R and other packages (such as gretl and the commercial EViews package). I have just encountered another problem and thought I'd post it to the list. In this case,

Zelig: Everyone's Statistical Software Kosuke Imai, Gary King and Olivia Lau Version 1.0 (Available at http://gking.harvard.edu/zelig) A growing proportion of statisticians and methodologists from many disciplines are converging on R, a powerful statistics package and programming language. As an open source project, R is freely

Dear R users, I am trying to estimate to estimate the average treatmen effect on the treated (ATT) using first the MatchIt software to weight the data set and, after this, the Zelig software as shown in Ho et al. (2007). See here for an explanation of how to apply this technique in R: http://imai.princeton.edu/research/files/matchit.pdf I encounter a slight problem when I apply the weights that

Hi dear sirs, I am wondering why the fit of the time serie x with an arima and the fit of diff(x) with an arma (same coeff p & d) differ one from another here are the output of R: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% > modelarma<-arma(diff(x),c(7,5)) > modelarma Call: arma(x = diff(x), order = c(7, 5)) Coefficient(s): ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 0.06078

Hello, I am attempting to "automate" a Bayesian normal linear regression using Zelig. Basically, I have a list containing several zelig() objects, each having a different formula, same data set, and same model (normal.bayes). My problem lies in the setx() method, where I am setting a numeric parameter to a value other than the mean. This is straightforward if I input the parameters

I've run an ordered logistic regression model in R with Zelig and am looking to calculate predicted probabilities. Zelig has a series of simple one line commands to generate the information I want on first differences and so forth. Unfortunately, I keep getting an error when running the zelig function and was wondering if there was a quick alternative for generating predicted probabilities for

Hi, I'm having problems with the Zelig package - when using the below R displays the follwing message (I'm running R i386 2.15.3 for Windows and have updated all the Zelig packages): z.out<-zelig(Surv(psurv2, pcens2) ~ ren_sup3 + age, data=data_urgent, model="coxph") ** The model "coxph" is not available with the currently loaded packages, ** and is not an

Hi there, I'm wondering what Zelig in the following situation (code below) actually does. Is this considered as a so called regression adjustment after the propensity score matching? library(MatchIt) library(Zelig) data(lalonde) re78 represents the outcome variable 1. With Zelig m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde)

Hello, would someone please help explain the following inconsistency in the if/else statement to me? The format of the if/else #3 below is ok, but if/else #1 is not? (I get an "unexpected else" type error.) In order for it to work I have to use if/else #2 Thanks .. maybe there is some reason for this, but this looks very inconsistent to me. R version 2.10.1 (2009-12-14) Ubuntu 9.04

install.packages works for Linux because the directory structure is not version dependent, as it is for the Windows download. That should be fixed as soon as my sys admin consents to install R 1.9.0 so we can do the cross compile. Of course, you are right, Martin and Peter, that Zelig should be on CRAN proper. We just need to write a few .Rd files to be compliant with R check, and change our

Dear all, Does anybody know why the following code returns an error message? >library(MatchIt) >library(Zelig) >data(lalonde) > >m.out1<-matchit(treat~age+educ+black+hispan+nodegree+married +re74+re75, method="full", data=lalonde) > >z.out1<-zelig(re78~age+educ+black+hispan+nodegree+married+re74+re75, data=match.data(m.out1, "control"),

Hi everyone, I have carried out a multiple imputation in R using Amelia II and have created 5 multiply imputed datasets. The purpose of my research is to fit a Poisson Model to the data to estimate numbers of hospital admissions. Now that I have 5 completed datasets and I have to pool all the 5 datasets to get one combined output for a poisson model. I have checked previous queries about

hello all I am using the R package Zelig for some tobit regression with robust standard errors. I have got R version 2.9.2 (2009-08-24) and Zelig Version: 3.4-5 when i do demo(robust) It ends like this way data(coalition) > # Fit the model with robust standard error > user.prompt() Press <return> to continue: > z.out3 <- zelig(Surv(duration, ciep12) ~ polar + numst2 +

On the topic of when to use "suggests" and "depends" and on R version requirements. I have cc'd this message to R-devel because I am curious about what senior developpers think about these issues. The problem arises because we are using some functions from the package "matchit" in a new version of our package "portfolio". We are listing the matchit in

Hello, I am writing as an administrator, not as an R user, so forgive me if I am not completely knowledgeable about R. I have a user who is creating an R package for windows from a Linux environment using the crossbuild environment by Jun Yan and A.J. Rossini. The packages she generated worked fine until she tried to install in R 1.9.1 for Windows. Now when she installs with

I'm running a multinomial logit in R using the Zelig packages. According to str(trade962a), my dependent variable is a factor with three levels. When I run the multinomial logit I get an error message. However, when I run 'model=logit' it works fine. any ideas on whats wrong? ## MULTINOMIAL LOGIT anes96two <- zelig(trade962a ~ age962 + education962 + personal962 + economy962 +