similar to: glm and Newey-West estimator

Hi, I want to calculate the t- and p-values for a linear model using the Newey West estimator. I tried this Code and it usually worked just fine: > oberlm <- lm(DYH ~ BIP + Infl + EOil, data=HU_H) > coeftest(oberlm, NeweyWest(oberlm, lag=2)) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1509950 0.0743832 2.0300 0.179486 BIP

Hello: My dataset set contains several thousand rows of data, with each row containing information for a house. The variables include the sale price of the house, the quarter and year of sale, the attributes of the house, and the attributes of the neighborhood and the city in which the house is located. The data is for a 10-year period. No house is repeated in the dataset. In summary, the dataset

Is anyone aware of a procedure to apply Newey-West corrections for autocorrelation to a SUR regression model? The SANDWICH package seems to be applicable only to LM or GLM models. Thanks, Richard Saba Department of Economics Auburn University Email: sabaric at auburn.edu

Dear all, I am looking for an equivalent to the "newey2"-extension in Stata, in order to compute Newey-West HAC standard errors in a regression using panel data. I would be very grateful for advice which R-package could do this. I thank you very much in advance. Dirius

I have a tiny R script performing two tasks, the first one of which may contain error. mammon(12)% cat z.R version options(show.error.messages = FALSE) try(b <- log("foo")) ## task 1 1 + 2 ## task 2 Running in batch mode from a solaries machine, the second task never got started; see below: mammon(13)% R BATCH --vanilla -q z.R mammon(14)% cat z.Rout >

dear R experts: ?I am writing my own little newey-west standard error function, with heteroskedasticity and arbitrary x period autocorrelation corrections. ?including my function in this post here may help others searching for something similar. it is working quite well, except on occasion, it complains that Error in solve.default(crossprod(x.na.omitted, x.na.omitted)) : system is

thank you, achim. I will try chol2inv. sandwich is a very nice package, but let me make some short suggestions. I am not a good econometrician, so I do not know what prewhitening is, and the vignette did not explain it. "?coeftest" did not work after I loaded the library. automatic bandwidth selection can be a good thing, but is not always. as to my own little function, I like the

Hi! Where to set R_LIBS? I have .Renviron with the line. R_LIBS=/package/R/R-1.5.0/linux/lib/R/library/;/home/wolski/Rpack/ in my home directory but cant access any library stored in the /home/wolski/Rpack/? Eryk -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

I'd like to program a simulation of recurrent events to test few models in R. Is there anybody who has experience of that and could give me advice? Your help is greatly appreciated! Regards, -- Peggy Ramlau ramlau at gmx.de +493080906358 Bitte l?cheln! Fotogalerie online mit GMX ohne eigene Homepage!

I downloaded geepack from your site and installed it from the zip file. However, although it appears under my library directory, I could not load into R. Regards John Sendak platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 6.0 year 2002 month 10

Hi R-helpers I have a dataframe DF (lets say with the variables, y, x1, x2, x3, ..., clust) containing relatively many NAs. When I fit an ordinal regression model with the function lrm from the Design library: model.lrm <- lrm(y ~ x1 + x2, data=DF, x=TRUE, y=TRUE) it will by default delete missing values in the variables y, x1, x2. Based on model.lrm, I want to apply the robust covariance

I have a dataframe that I'm trying to reshape, and need advice. My data: > klam.merge[200:225,] stream lulc x sumlength pct.lgth 200 1223030419685 92 0.25000000 9.89 2.52780586 201 1223030419686 23 0.00274154 4.73 0.05796068 202 1223030419686 41 0.75009917 4.73 15.85833341 203 1223030419686 42 2.65000000 4.73 56.02536998 204

Where can I find a package with an R implementation of the S-Plus "nlmib" general minimzation routine described in Venables & Ripley's "Modern Applie Statistics with S-Plus", 3rd Ed., pp 267 - 270? Thanks, David Stamps stamps.d.a at att.net -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, Does anyone read Verbeek's "A Guide to Modern Econometrics"? In its Section 4.11, how does the last two equations' HAC calculate? I've tried several groups of parameters in sandwich::NeweyWest, but I still cannot get the same result. I've tried lag=2 and lag=3, as long as prewhite=FALSE and prewhite=TRUE yet, but... Sincerely Hsiao-nan Cheung

Dear R-help, I would like to be able to run the following code sequence as a source routine. If I paste it into R via the clipboard it works as expected, but if I source the code instead then the last 3 statements fail. I've also tried writing to the file in place of the sink sequence, but that also hits a snag. R 1.6, w98e2, dfr is a data frame containing the content of Julian

Dear all, I am working on a large dataset and need to use biglm() to perform OLS regressions. I have detected significant ARCH effects which I try to account for using the Newey-West correction. So far, I have worked with NeweyWest() in the sandwich package. NeweyWest() however seems to be unable to handle an object of class "biglm". Looking into the code, I figured out that

Hi, I have a dataframe containing data from individuals 1, ..., 12 (grouping variable "g" in the data frame below), which belong either to "A" or "B" (grouping variable "f"): set.seed(1) tmp <- data.frame(

I want to calculate Newey West robust standard error using NeweyWest. Comparing the results to what I get in STATA, in order to get the same results in I need to specify "prewhite=0". Can someone explain what this prewhite command means? Thanks [[alternative HTML version deleted]]

Hi, I have two sets of data that I would like to put into a data frame. But since they have different length, I am not sure how to do this. Here is an example of my data: data set one: date growth 1/1/2007 10 1/2/2007 10.2 1/3/2007 10.4 1/4/2007 10.6 data set two: date growth 1/1/2007 22 1/2/2007 22.5 1/4/2007 22.4 I would like to combine the two data sets and

I have a spatial data set with ordinal response variable containing four levels. I would like to know if and how spatial autocorrelation can be taken into account when ordinal logistic regression is used (e.g. the function lrm from the Design package). Thanks for your help! Christof