similar to: help for dynlm command

Hi, I was wondering why the results from lm and dynlm are not the same for what I think is the same model. I have just modified example 4.2 from the Pfaff book, please see below for the code and results. Can anyone tell my what I am doing wrongly? Many thanks, Werner set.seed(123456) e1 <- rnorm(100) e2 <- rnorm(100) y1 <- ts(cumsum(e1)) y2 <- ts(0.6*y1 + e2) lr.reg <- lm(y2

Dear Professor Bernhard, Sorry for take your time, but I found something strange that I am not able to explain/understand. Suppose that I compute the ADF test by using the criterion="BIC" to select the lags: summary(CADFtest(y, max.lag.y = 20, type = "drift", criterion="BIC")) Suppose that 2 lags are selected. Next, if I set the lags to 2: summary(CADFtest(y,

Hello: I am a rookie in using R. When I used the unit root test in "CADFtest", I got the different t-test statistics between using criterion="BIC" and no using criterion. But when I checked the result with eviews, I find out that no using criterion is correct. Why after using criterion="BIC", I got the different result? Paul > data(Canada) > ADFt

I want to set up a model with a formula and then run dynlm(formula) because I ultimately want to loop over a set of formulas (see end of post) R> form <- gas~price R> dynlm(form) Time series regression with "ts" data: Start = 1959(1), End = 1990(4) <snip> Works OK without a Lag term R> dynlm(gas ~ L(gas,1)) Time series regression with "ts" data: Start =

Hello, I would like to write a program that compute the principal components of a set of data and then 1. Run the dependent variable against the principal components (lagged value) 2. Do prediction , following Stock and Watson (1999) "Forecasting Inflation". All data are time series. Now I can run the program using single factor (first principal component), but I

Dear useRs, I've release a new version of the "dynlm" package to CRAN which adds two new features: o instrumental variables regression (two-stage least squares) via formulas like dynlm(y ~ x1 + x2 | z1 + z2 + z3, data = mydata) where z1, z2, z3 are the instruments which can again contain lags/differences/season via the d()/L()/season() operators. o

All: How do I obtain one step ahead out-of-sample forecasts from a model using "dyn" or "dynlm" ? Thanks! Best, John [[alternative HTML version deleted]]

Dear useRs, recently, there were several discussions on R-help about how to conveniently fit dynamic linear models and time series regressions. The package dynlm tries to address this problem by 1. providing some more functions like lags L() and differences d() and season() in the formula specification of a model and 2. preserving the time series attributes of the data. A first version of the

Dear useRs, recently, there were several discussions on R-help about how to conveniently fit dynamic linear models and time series regressions. The package dynlm tries to address this problem by 1. providing some more functions like lags L() and differences d() and season() in the formula specification of a model and 2. preserving the time series attributes of the data. A first version of the

Each of the data sets contains monthly observations on price indices for 7 countries. I use the fitted values from reg1 in the reg2 model. The interior loop executes without error as long as I explicitly specify the data set, i.e. data=dat70. However the code fails to execute if I specify the model in the form of the commented line, i. e reg1 <-dynlm(form1,data=Dnames[j]) I get the following

Hello, I have a question on unit root test with urca toolbox. First, to run a unit root test with lags selected by BIC, I type: > CPILD4UR<-ur.df(x1$CPILD4[5:nr1], type ="drift", lags=12, selectlags ="BIC") > summary(CPILD4UR) The results indicate that the optimal lags selected by BIC is 4. Then I run the same unit root test with drift and 4 lags:

Hi, I have a problem with a regression I try to run. I did an estimation of the market model with daily data. You can see to output below: /> summary(regression_resn) Time series regression with "ts" data: Start = -150, End = -26 Call: dynlm(formula = ror_resn ~ ror_spi_resn) Residuals: Min 1Q Median 3Q Max -0.0255690 -0.0030378 0.0002787

Does anyone have an exemple of how to fit a SARIMA model , with a MA part, with the package dynlm? Best regards. --------------------------------- [[alternative HTML version deleted]]

Dear All, I'm trying to use dynlm to fit a time series. I have 3 seasonal terms. Here is an example of the problem. This is my time variable, hourly data: timeSeries <- seq(as.POSIXct("2011-01-01 00:00:00"), as.POSIXct("2011-12-31 23:00:00"), by="hour") My response is: y <- rnorm(length(t), 1000, 500) There are 3 seasonal factors: t.h <-

Dear All, I'm trying to use dynlm to fit a time series. I have 3 seasonal terms. Here is an example of the problem. This is my time variable, hourly data: timeSeries <- seq(as.POSIXct("2011-01-01 00:00:00"), as.POSIXct("2011-12-31 23:00:00"), by="hour") My response is: y <- rnorm(length(t), 1000, 500) There are 3 seasonal factors: t.h <-

take this code library(dynlm) test=dynlm(diff(log(y))~ lag(x, -1) + lag(z, -1)) fc=predict(test) fc when I run this and look at "fc", the values there only go as long as the sample (of y) goes, but it should be one further, right? since the other values are lagged. Is predict the wrong function to reach this? Thank you -- View this message in context:

I want to calculate the commulative sum of any numeric vector with the following command but this following command does not work "comsum" My question is , how we can calculate the commulative sum of any numeric vector with above command Thanks Zahid Khan Lecturer in Statistics Department of Mathematics Hazara University Mansehra. ---------------------------------

Hi all, I've just started working my way through Mike West and Jeff Harrison's _Bayesian Forecasting and Dynamic Models_, and I was wondering if there were any publically-available packages to handle dynamic linear models, as they describe. I found the "dynlm" package, but either I don't yet understand what's going on or that package uses a different sense of the phrase

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Hello, So I am afraid I am having a recurring problem that I just can't figure out. I am using the plm package to conduct a panel analysis - although I am not sure if the problem is arising as a result of the plm package or something more general. I am trying to run a fixed effects model with effects over time and individual. The model has various lags, and the problem is that these lags do