search for: autoregression

I have not been able to find any programs for running vector autoregressions with R. I am interested in running Bayesian VARs and also running VARs that run all combinations of variables in the vector. Is anyone currently developing this? -Nirav Mehta

Dear R users, I would like to simulate, for 20000 replications, an autoregressive process: y(t)=0.8*y(t-1)+e(t) where e(t) is i.i.d.(0,sigma*sigma), Thank you in advance ____________________________________________________ Écoutez gratuitement le nouveau single de Noir Désir et découvrez d'autres titres en affinité avec vos goûts musicaux

Dear R users, I am trying to find a package that can estimate an autoregressive model for discrete data. I am imagining a Poisson or Gamma process in which the mean (say mu) follows a process such as mu_t = a + b*x + c*mu_{t-1} Suppose I have data on the time-series Poisson outcomes and x and would like to obtain ML estimates for b and c. Does anyone know of a package that can do this

Hello everybody, I've a question about "autoregressive Regressionmodels". Let Y[1],.....,Y[n], be a time series. Given the model: Y[t] = phi[1]*Y[t-1] + phi[2]*Y[t-1] + ... + phi[p]*Y[t-p] + x_t^T*beta + u_t, where x_t=(x[1t],x[2t],....x[mt]) and beta=(beta[1],...,beta[m]) and u_t~(0,1) I want to estimate the coefficients phi and beta. Are in R any functions or packages for

Hello - I need to do simple linear autoregressive model with R software for my thesis. I looked into all your documentation and I am not able to find anything too helpful. Can someone help me with the codes? Thanks Emil [[alternative HTML version deleted]]

Hello, I am trying to carry out a phylogenetic autoregression to test whether my data show a phylogenetic signal, but I keep calculating bizzare R-squared values. My script is: > library(ape) > x <- "((((((((R:1,GK:1)d:1,(MW:1,G:1)n:1)a:1,SPW:1)a:1,WB:1)b:1,(((SPBF:1,PBF:1)n:1,(HBF:1,SWF:1)c:1)i:1,(PE:1,((P:1,C:1)w:1,(MF:1,HF:1)l:1)b:1)c:1)x:...

...ve models of successively higher orders up to lag.max. " However, R seems to return the Yule-Walker estimates of the PACF by default. You can check this using c(1:10) as the series: the YW estimates are 0.7000000 and -0.1527035 for lags 1 and 2 . If the PACF really was estimated from running autoregressions, the first coefficient should be 1 and one would expect an error message for higher-order coefficients (due to singularity of the regression matrix). pacf() or acf (..., type="partial", ...), however, return 0.7000000 and -0.1527035. Best regards, Christian Kleiber -------------- next...

Hi all ! I would like to simulate spatial lattice/areal data with a conditional autoregressive (CAR) structure, for a given neighbouring matrix and for a autocorrelation "rho". Is there any package or function in R to perform it ? I found the function "CARsimu" in the hdeco library, but this is not what I'm looking for Thanks in advance Dae-Jin --

Hey, all, I may just be missing something, but I'm trying to construct a temporal autoregression with an independant variable other than just what is happened at a previous point in time. So, the model structure would be something like y(t)=b0+b1*y(t-1)+b2*y(t-2)...+a*x(t) I'm even considering a model of y(t)=b0+b1*y(t-1)+b2*y(t-2)...+a1*x(t)+a2*x(t-1)... So, my data looks like Tim...

Hi, I am trying to replicate Engle and Manganelli's paper Conditional Autoregressive Value at Risk (CAViaR) by Regression Quantiles. I have the Matlab code which I cannot get to work as I have never used Matlab before, does anyone know if there is the same code available to estimate the CAViaR models in R? Thanks, Shane -- View this message in context:

Hi, I am trying to fit all subsets for a vector autoregression with exogenous variables. I have been looking at the 'leaps' function but I not sure how to get it to work when lags for each variable are included in the model. I would be really appreciative if someone could provide some links to examples. Thanks in advance! -- View this message in c...

Hello, I am using R for fitting parameters of a time series model. The model is as below. Y(t) = mu + a*X(t) + YN(t) where YN(t) = b*YN(t-1) + innovation and Z(t) follows N(0,1). The main obstacle for me is the autoregressive error term, YN(t). I can't figure out how to estimate the parameters (mu, a, b) with usual 'arima' function in R. What I have tried is.... 1. Do the

Dear all, I would like to implement a spatial quantile regression using instrumental variable estimation (according to Su and Yang (2007), Instrumental variable quantile estimation of spatial autoregressive models, SMU economics & statistis working paper series, 2007, 05-2007, p.35 ). I am applying the hedonic pricing method on land transactions in Luxembourg. My original data set contains

Dear David, Dear R Users , Suppose that we want to regress for example a certain autoregressive model using SVM. We have our data and also some fixed kernels in libSVM behinde e1071 in front. The question: Where can we insert our certain autoregressive model ? During creating data frame ? Or perhaps we can make a relationship between our variables ended to desired autoregressive model ?

This might be obvious to some, but I can't find a neat way to do it: Say I have two (very long) numerical vectors a & b of the same length representing variable coefficients of a linear autoregression. I want to calculate vector x defined by x[1] <- b[1] for (n in 2:length(a)) x[n] <- a[n]*x[n-1] + b[n] Is there a way to do this vectorially, i.e. without using the 'for' loop? Thanks, Sandor

Is there any R package that implements a bifurcating autoregression, aka the BAR(n) model? I've been reading the Huggins and Staudte paper, "Variance Components Models for Dependent Cell Populations", from the Journal of the American Statistical Association, 1994. Shawn Garbett <shawn.p.garbett at vanderbilt.edu> Vanderbilt Cancer Biolog...

I wish to estimate sparse causal networks from simulated time series data. Although there's some discussion about this problem in the literature (at least a few authors have used lasso and l(1,2) regularization to enforce sparsity in multivariate autoregressive models, e.g., http://user.cs.tu-berlin.de/~nkraemer/papers/grplasso_causality.pdf), I can't find any R packages with these

....682507e-03 1.905265e-02 3.030691e-04 FUM1V 19910228 ... ... FUM1V 19910327 ... ... FUM1V 19910430 ... ... NOK1V 19910228 NOK1V 19910327 NOK1V 19910430 Hope someone can help. -Tom -- View this message in context: http://www.nabble.com/Vector-autoregression%2C-panel-data-tp20679144p20679144.html Sent from the R help mailing list archive at Nabble.com.

Dear All, I have a short question. Is there any readily available function that could generate either an ARMAX model or, more generally, an AutoRegressive Distributed Lag model? I am looking for a function that is similar to armaSim() function in fArma package. Thank you. MP

folks, does anyone know if the SEMIFAR model has been implemented in R? i see that there's a S-FinMetrics function SEMIFAR() that does the job, but I have no access to that software. essentially, this semiparametric fractional autoregressive model introduces a deterministic trend to the FARIMA(p,d,0) model (which, as i understand it, takes care of the random trend and short and long memory).