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...e matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo *************************************** Carlo Fezzi Senior Research Associate Centre for Social and Economic Research on the Global Environment (CSERGE), School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ United Kingdom. email: c.fezzi at uea.ac.uk *************************************** Here is an example with 3 equatio...

...tim" does not allow this argument which, on the other hand, is allowed in "optim". I would be extremely grateful if anybody could suggest a way I could use to I obtain the values of the hessian matrix... Many thanks, Carlo ************************************************** Carlo Fezzi Senior Research Associate Centre for Social Research on the Global Environment (CSERGE) Department of Environmental Sciences University of East Anglia Norwich (UK) NR2 7TJ Telephone: +44(0)1603?591408 Fax: +44(0)1603 593739

...(still 5000 obs) more than 1 hour. Is there any way I can speed up this process? (Modifying the code or modifying some R options maybe?) I would be really grateful if anybody could help me with this issue, I attach my code below. Many thanks, Carlo *************************************** Carlo Fezzi Centre for Social and Economic Research on the Global Environment (CSERGE), School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ United Kingdom. *************************************** # MULTILOGIT # This function computes the estimates of a multinomial logit model...

Dear helpers, Does anyone know about a package or a function that allows to estimate Multivariate Markov-Switching Models, like MS-VAR as introduced by Krolzig(1997) with R ? Thanks a lot!! Carlo

Dear all, I am writing an R code to fit a Bayesian mixed logit (BML) via MCMC / MH algorithms following Train (2009, ch. 12). Unfortunately, after many draws the covariance matrix of the correlated random parameters tend to become a matrix with almost perfect correlation, so I think there is a bug in the code I wrote but I do not seem to be able to find it.. dull I know. Has anybody written a

Dear all, I am using the "mgcv" package by Simon Wood to estimate an additive mixed model in which I assume normal distribution for the residuals. I would like to test this model vs a standard parametric mixed model, such as the ones which are possible to estimate with "lme". Since the smoothing splines can be written as random effects, is it correct to use an (approximate)

...on "lm" considering the residuals indipendent (this is true both in and out of sample). Does it means that "predict" doesn't use the information about the residual correlation to improve predictions? How can I overcome this problem? Thank you so much for your help, Carlo Fezzi

Hello, I am a newbie to Linux, i actually use Redhat 9, some times i encounter this problem on squid (squid is dead but PID exist) can any one tell me what the couse is and how to handle this problem without reinstallation. Thanks Fezzy. __________________________________ Yahoo! Messenger Show us what our next emoticon should look like. Join the fun.

...u2) c.s <-c(rep(0,N-1),rep(c(low,-high),N)) #### #### OPTIMIZATION a<-constrOptim(outer.iteration = 500, control = list(maxit=10000), theta=(high-low)/(N+1)* N:1, f=negdet, ui = u.s, ci=c.s, method="Nelder-Mead") #### ************************************************** Carlo Fezzi Senior Research Associate Centre for Social Research on the Global Environment (CSERGE) Department of Environmental Sciences University of East Anglia Norwich (UK) NR2 7TJ Telephone: +44(0)1603?591408 Fax: +44(0)1603 593739

Dear helpers, does anyone know a function to fit a model with: - y mean that is regressed on a set of explanatory variables - y variace behaving as a garch or as a garch in mean Thank you so much for your help, Carlo

Dear R - helpers, I am using the urca package to estimate cointegration relations, and I would be really grateful if somebody could help me with this questions: After estimating the unrestriced VAR with "ca.jo" I would like to impose the rank restriction (for example rank = 1) and then obtain the restricted estimate of PI to be utilized to estimate the VECM model. Is it possible? It