similar to: How to do FMOLS and DOLS?

Whether can any R package run Full modified OLS (Phillips and Hansen 1990 ), DOLS (Stock and Watson 1993) and ADL model (Pesaran and Shin 2001) for cointegrated VAR model? I cannot find any useful order in VAR and SVAR package. Thanks. Eric Wang [[alternative HTML version deleted]]

Is anyone aware of an R package that implements Pedroni's FMOLS (Fully Modified Ordinary Least Squares) package, for panel data with non-stationary and co-integration?

Hello I am facing a curious problem.I have a time series data with which i want to fit auto-regressive model of order p, where p runs from 1:9.I am using a for loop which will fit an AR(p) model for each value of p using the *ar.ols* function. I am using the following code for ( p in 1:9){ a=ar.ols (x=data.ts, order.max=p, demean=T, intercept=T) } Specifying the *order.max* to be p, it gives me a

Hello, I have a question about the ?plot.predict? function in Frank Harrell's rms package. Do you know how to superpose in the same graph the prediction curve of ols and raw data points? Put most simply, I would like to combine these two graphs: > fit_linear <- ols (y4 ~ rcs(x2,c(5,10,15,20,60,80,90)), x=TRUE, y=TRUE) > p <- Predict(fit_linear,x2,conf.int=FALSE) > plot (p,

I am cleaning up the rms package to not export functions not to be called directly by users. rms uses generic functions defined in other packages. For example there is a latex method in the Hmisc package, and rms has a latex method for objects of class "anova.rms" so there are anova.rms and latex.anova.rms functions in rms. I use:

Hi there, we are beginners in R and we are trying to fit the following time series using ar(2): > x <- c(1.89, 2.46, 3.23, 3.95, 4.56, 5.07, 5.62, 6.16, 6.26, 6.56, 6.98, > 7.36, 7.53, 7.84, 8.09) The reason of choosing the present time series is that the we have previously calculated analitically the autoregressive coefficients using the direct inversion method as 1.1, 0.765, 0.1173.

Dear R users, I'm trying to carry out monte carlo integration of a posterior density function which is the product of a normal and a gamma distribution. The problem I have is that the density function always returns 0. How can I solve this problem? Here is my code #generate data x1 <- runif(100, min = -10, max = 10) y <- 2 * x1^2 + rnorm(100) # # # # # # # # Model 0 # # # # # # #

Hi I have a problem on how to proceed with further steps in my analysis. I did a linear OLS regression (ri,t=alpha*beta*rm,t+et) with my daily data of stock and index returns. There is now the problem of arch in my error terms. Thus I used the following r command: /garch(resid_desn, order=c(0,2)) ## This ARCH(2) process seems to fit the best after trial and error. Consequently, I get there

Using platform x86_64-pc-linux-gnu arch x86_64 os linux-gnu system x86_64, linux-gnu status major 2 minor 13.0 year 2011 month 04

Hi all ARCH modelling I have a problem now on how to proceed with further steps in my analysis. I did a linear OLS regression with my daily data of stock and index returns. There is now the problem of arch in my error terms. Thus I used the following r command: garch(resid_desn, order=c(0,2)) ## This ARCH(2) process seems to fit the best after trial and error. Consequently, I get there three

Hi, so I don't speak computer and I have no idea what this code is telling the program to do, but I apparently need to be able to find and isolate influencial observations. Problem, I have no idea what the error means and where it may be from in the code. error I get is below the code { ## OLS results NameC<- lm(gpanew~female+female:lastinit+agenew+canadian+mom_ed+yearstudy) ## default:

Hi there, I am really new to statistics in R and statistics itself as well. My situation: I ran a lot of OLS regressions with different independent variables. (using the lm() function). After having done that, I know there is endogeneity due to omitted variables. (or perhaps due to any other reasons). And here comes the Hausman test. I know this test is used to identify endogeneity. But what I

Hi, can anybody tell me how the Hausman test for endogenty works? I have a simulated model with three correlated predictors (X1-X3). I also have an instrument W for X1 Now I want to test for endogeneity of X1 (i.e., when I omit X2 and X3 from the equation). My current approach: library(systemfit) fit2sls <- systemfit(Y~X1,data=data,method="2SLS",inst=~W) fitOLS <-

Dear R help, I'm having some trouble with model formulas for the ols function in the rms package. I want to have two variables represented as restricted cubic splines, and also include an interaction as a product of linear terms, but I get an error message. library(rms) d <- data.frame(x1 = rnorm(50), x2 = rnorm(50), y = rnorm(50)) ols(y ~ rcs(x1,3) + rcs(x2,3) + x1*x2, data=d) Error in

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

Hi there, I tried it many times but didn't get it worked. I just want to export the summary of a OLS regression (lm() function) into a csv-file including the "call"-formula", "coefficients", "r-squared", " adjusted r-squared" and "f statistic". I know I can export: write.csv2(Regression_60d_ann$coefficients,

Hi...Can anyone help me for adding the second regressor to the FMOLS regresion. I am using the codes below. I could not succeed to write the correct sentence in place of the question marks. Do I need to make any changes in the source file also? I attached the original files published by Bruce Hansen. Thank you very much in advance for your precious help. Best Regards, Tarkan source

Hi folks! Does anybody know how to use the OLS function in fMultivar to do dynamic regression? I've tried specifying lags in OLS using a data series created in fSeries and it doesn't seem to work. I've done dynamic regression using dyn$lm and I was wondering how to accomplish the same thing using the OLS function from fMultivar. Thanks! John [[alternative HTML version

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

How can I extract the adjusted R^2 value from an ols object (using rms package)? library(rms) x <- rnorm(10) y <- x + rnorm(10) ols1 <- ols(y ~ x) Typing "ols1" displays adjusted R^2 among other things, but how can I assign it to a variable? I tried str(ols1) but couldn't see where to go from there. Thanks, Mark Seeto