similar to: function censReg in panel data setting

Hi there, I need to run a Tobit Fixed Effects in a panel data with 4500 units for 8 years. It is a huge data set, my dependent variable is left truncated at zero, the distribution is skewed and my panel is balanced. Any suggestions on how to do that in R? I tried stuff like survreg, censReg, and tobit but none of them were satisfactory. Thanks, *Felipe Nunes* CAPES/Fulbright Fellow PhD

Hi - First post, so excuse any errors in protocol: Wanted to ask if there's an easy way to use 'predict' with objects of class 'censReg', 'maxLik', 'maxim' or 'list'. Have a left-censored dataset, attempting to use a Tobit model and am working with the censReg package. I like how easy it is to move from glm models to predictions with

Hello! I'm thinking of applying a censored regression model to cross-sectional data, using either the tobit (package survival) or the censReg function (package censReg). The dependent variable is left and right-censored. My hopefully not too silly question is this: I understand that heteroskedasticity and nonnormal errors are even more serious problems in a censored regression than in an

Hola Javier, Si, cuando hablo de valor observado me refiero al valor real en campo y el predicho al que estiman los modelos. Disculpa, que no lo detallase así desde el principio. En mi caso trabajo con dos diferentes: Zero inflated y Binomial Negativo y me gustaría comprobar que diferencia (distancia) existe entre cada uno de ellos y la realidad. Estoy trabajando con los siguientes paquetes:

Appreciate any suggestions regarding how to fit an unbalanced panel data to a Tobit model using R functions. I am trying to analyze how real estate capital expenditures (CapEx) are affected by market conditions using a panel Tobit model. The CapEx is either positive or 0, so it is censored. The data are unbalanced panel, including the CapEx of about 5000 properties over about 40 quarters, with the

Hello everyone, I am working with plm package and I have problem with random and within models, which are giving errors which says "empty model". However, the model is not empty. In the source code for plm.fit, where the error originates it says something like (writing from the top of my head...) X <- model.matrix(formula,data, lhs=1,...) if (ncol(X) == 0) stop("empty

Dear R users, Could you tell me where a I find some references of BHHH algorithm ? I need write it in R. Thank you.

First of all, I have GAMs noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian (link=log),groupD,methods=REML) Then I type " summary(noxd)". and show Family: gaussian Link function: log Formula: newNO2 ~ pressure

I am currently trying to find MLE of a function with four parameters. My codes run well but i don't get the results. I get the following message: BHHH maximisation Number of iterations: 0 Return code: 100 Initial value out of range. I don't know this is so because of the way i have written my loglikelihood or what. The loglikelihood LogLik<-function(param){ beta_1<-param[1]

Dear R users, I am new to R. I would like to find *maximum likelihood estimators for psi and alpha* based on the following *log likelihood function*, c is consumption data comprising 148 entries: fn<-function(c,psi,alpha) { s1<-sum(for(i in 1:n){(c[i]-(psi^(-1/alpha)*(lag(c[i],-1))))^2* (lag(c[i],-1)^((-2)*(alpha+1)) )}); s2<- sum(for(m in 1:n){log(lag(c[m],-1)^(((2)*alpha)+2))});

Hello R community, I have been using R's inbuilt maximum likelihood functions, for the different methods (NR, BFGS, etc). I have figured out how to use all of them except the maxBHHH function. This one is different from the others as it requires an observation level gradient. I am using the following syntax: maxBHHH(logLik,grad=nuGradient,finalHessian="BHHH",start=prm,iterlim=2)

Have a look at ?optim. I don't think it has the BHHH algorithm as an option, though. =========================================== David Barron Jesus College University of Oxford -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Harold Doran Sent: 10 July 2003 15:43 To: Fohr, Marc [AM]; R-help at stat.math.ethz.ch

Buenos días a todxs, Estoy comparando la predicción de los valores (0, 1, 2, 3,.....hasta 13) frente a los observados. Con la idea de comparar el modelo Zero inflated y el Binomial negativo y ver cual presenta mas distancia frente a las predicciones observadas. Para ello introduzco los códigos en la consola: #Modelo ZIM pred<-round(colSums(predict(zeroinfl, type="prob") [,1:14]))

Dear all, I am having difficulties using the seemingly-simple function lag. I have a dataframe with several weather variables (maxitemp, windspeed, rainfall etc), and the response variable (admissions). The dataset is fairly large (1530 observations). I simply want to model the response against a lag of a couple of the explanatory variables, say maxitemp and rainfall. I would like to look at

Dear list, this is not a particular R question but perhaps someone can help. I am running a maximum likelihood estimation (competing risk duration model with unobserved heterogeneity) on 30 different datasets. The problem is that on 2 datasets the model does not converge. I am interested if there are any methods, based on the gradients or (an approximation of) the hessian which helps to

Dear all, I am modeling the incidence of recreational anglers along a stretch of coastline, and with a vary large proportion of zeros (>80%) have chosen to use a zero inflated negative binomial (ZINB) distribution. I am using the same variables for both parts of the model, can anyone help me with R code to compute overall marginal effects of each variable? My model is specified as follows:

I'm having trouble using lattice and Sweave together. An example of the trouble is the chunk <<fig=true, eps=false>>= library(nlme) data(Orthodont) plot(Orthodont) @ This gives a warning Warning message: No Active Device, using default values in: trellis.par.get("add.text") and creates a pdf file with no pages. I think this is related to the fact that a figure

Hello, I am having some difficulty converting my gam code to a correct gamm code, and I'm really hoping someone will be able to help me. I was previously using this script for my overdispersed gam data: M30 <-gam(efuscus~s(mic, k=7) +temp +s(date)+s(For3k, k=7) + pressure+ humidity, family=negbin(c(1,10)), data=efuscus) My gam.check gave me the attached result. In order to

Hello, i want to estimate a complex GARCH-model (see below). http://r.789695.n4.nabble.com/file/n4112396/GJR_Garch.png W stands for the Day of the Week Dummies. r stands for returns of stock market indices. I stands for the GJR-term. I need some help with three problems: 1.) implementation of the GJR-term in the variance equation 2.) compute robust covariance matrix

Hi all, I have a large dataset with ~8600 observations that I want to compress to weekly means. There are 9 variables (columns), and I have already added a "week" column with 51 weeks. I have been looking at the functions: aggregate, tapply, apply, etc. and I am just not savvy enough with R to figure this out on my own, though I'm sure it's fairly easy. I also have the Dates