similar to: Conditional Autoregressive Value at Risk (CAViaR)

How is it possible to estimate the conditional autoregressive Value-at-Risk model qantile_t(tau)=a0+a1*qantile_(t-1)(tau)+a2*abs(r_(t-1)) see http://www.faculty.ucr.edu/~taelee/paper/BLSpaper1.pdf (page 10)) of Engle & Manganelli in R? The qantile_(t-1)(tau)-term causes headache. Kind regards, Jaci -- "Ein Herz f?r Kinder" - Ihre Spende hilft! Aktion: www.deutschlandsegelt.de

Hi list, I googled around but couldn''t find anything on whether someone has good or bad experiences with the Caviar *Blue* drives? I saw in the archives Caviar Blacks are *not* recommended for ZFS arrays (excluding apparently RE3 and RE4?). Specifically I''m looking to buy Western Digital Caviar Blue WD10EALS 1TB drives [1]. Does anyone have any experience with these drives? If

Mr./Ms. Thank your help I need the code of quantile regression - estimation of CAViaR, would do you like to help me! regards, tangyong school of managemnet ,fuzhou university, China [[alternative HTML version deleted]]

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 --

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 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 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 - 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]]

I know that this has been well-discussed already, but it''s been a few months - WD caviars with mpt/mpt_sas generating lots of retryable read errors, spitting out lots of beloved " Log info 31080000 received for target" messages, and just generally not working right. (SM 836EL1 and 836TQ chassis - though I have several variations on theme depending on date of purchase: 836EL2s,

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).

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

I am having difficulty fitting a mgcv::gamm model that includes both a random smooth term (i.e. 'fs' smooth) and autoregressive errors. Standard smooth terms with a factor interaction using the 'by=' option work fine. Both on my actual data and a toy example (below) I am getting the same error so am inclined to wonder if this is either a bug or a model that gamm is simply unable

Hi all, I am sorry for bothering the list, but I hope one of its members can help me. Currently I am doing a spectral analysis with the help of R. The spectral density I have calculated as follows: (The vector q contain some testing numbers.) > q <- c(28,28,26,19,16,24,26,24,24,29,29,27,31,26,38,23,13,14,28,19,19,17,22,2,4,5,7,8,14,14,23,23) > N <- length(q) > Fourier <-

Greetings: I am a relatively new user to R. I was wondering if anyone is familiar with MATLAB's far_g() function. If yes, is there an R equivalent to this? I would like to have the ability to input as my observation vector continuous values. I noticed that there was something close in R, sar_probit_mcmc(), but I can only use a binary vector as my observation vector. If my

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

Hi, I would like to make a replication of 10 of a linear, first order Autoregressive function, with respect to the replication of its innovation, e. for example: #where e is a random variables of innovation (from GEV distribution-that explains the rgev) #by using the arima.sim model from TSA package, I try to produce Y replicates, with respect to every replicates of e, #means for e[,1], I want

Hello to all- 1. When I run a regression which implements the augmented Dickey-Fuller test, I am confused about the names given to the regressors in the output. I understand what "xGE" stands for in a standard "lm" test involving an independent variable GE for instance, but if I lags and or differences are included in the model, what do the following "output" stand

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