similar to: Forecasting with Panel Data

Hi, I'm trying to plot the forecasts I generated using the Plot.Fore function of the BootPR package. But I got an error from zoo: My data: Time Series: Start = 1 End = 18 Frequency = 1 [1] 38731 38628 39117 92809 71984 31226 58613 72360 107956 92066 [11] 95208 99098 95848 120383 110717 105680 98469 101916 Script: y1<-ts(y1);

Dear Users, I would like to simulate an AR(1) (y_t=ct1+y_t-1+e_t) model in R where the innovations are supposed to follow a t-GARCH(1,1) proccess. By t-GARCH I want to mean that: e_t=n_t*sqrt(h_t) and h_t=ct2+a*(e_t)^2+b*h_t-1. where n_t is a random variable with t-Student distribution. If someone could give some guidelines, I can going developing the model. I did it in matlab, but the loops

Dear All, Following Zhang instructions I add the environmental variables, but now I receive the following message: > library(tcltk) Error in firstlib(which.lib.loc, package) : Can't find a usable init.tcl in the following directories: { C:\tcl\lib\tcl8.3} This probably means that Tcl wasn't installed properly. Error in library(tcltk) : .First.lib failed I'm using R Win

Hi Folks, Please, when I ask the option reg.line at the scatterplot in package car, the OLS models includes a constant? If not how can I do it sing the following code: scatterplot(lfirms ~ lscale, data=dataset, reg.line=lm, smooth=FALSE, labels=FALSE, span=0.5, xlab="Relative Plant Fixed Cost", ylab="Relative Number of Firms", pch=c(18),

Dear Helplist, I want to produce forecasts from a structural equation model. With the SEM package the model setup and its estimation is possible. However, I have not figured out how to obtain ex ante forecasts, i.e. applying the Gauss-Seidel algorithm to the estimated structural equations for provided values of the exogenous variables (i.e.: y_t = -inv(A)*B*x_t). Does anyone know if the there is

Hello, Is there a package or sample code that shows how to do ex ante forecasts with a macroeconomic structural equations model? I looked at the "sem" package, which lets you estimate e.g. Klein's model, but I'm not sure how to make simulations using the full set of equations, including the identities. Thank you, Ronaldo Carpio rncarpio at yahoo.com

Hi Frances, I have not touched the system.fit package for quite some time, but to solve your problem the following two pointers might be helpful: 1) Recast your model in the revised form, i.e., include your identity directly into your reaction functions, if possible. 2) For solving your model, you can employ the Gau?-Seidel method (see https://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method).

Who was speaking about non-linear models in the first place??? The Klein-Model(s) and pretty much all simultaneous equation models encountered in macro-econometrics are linear and/or can contain linear approximations to non-linear relationships, e.g., production functions of the Cobb-Douglas type. Best, Bernhard -----Urspr?ngliche Nachricht----- Von: Berend Hasselman [mailto:bhh at xs4all.nl]

Frances, I would not advise Gauss-Seidel for non linear models. Can be quite tricky, slow and diverge. You can write your model as a non linear system of equations and use one of the nonlinear solvers. See the section "Root Finding" in the task view NumericalMathematics suggesting three packages (BB, nleqslv and ktsolve). These package are certainly able to handle medium sized models.

Hello, I have estimated a simultaneous equation model (similar to Klein's model) in R using the system.fit package. I have an identity equation, along with three other equations. Do you know how to explicitly identify the identity equation in R? I am also trying to forecast the dependent variables in the simultaneous equation model, while incorporating the identity equation in the

> On 13 Jul 2017, at 12:55, Pfaff, Bernhard Dr. <Bernhard_Pfaff at fra.invesco.com> wrote: > > Who was speaking about non-linear models in the first place??? > The Klein-Model(s) and pretty much all simultaneous equation models encountered in macro-econometrics are linear That's really not true. Klein model is linear but Oseibonsu did not say that explicitly. "Klein

I'm currently working with some time series data with the xts package, and would like to generate a forecast 12 periods into the future. There are limited observations, so I am unable to use an ARIMA model for the forecast. Here's the regression setup, after converting everything from zoo objects to vectors. hire.total.lag1 <- lag(hire.total, lag=-1, na.pad=TRUE) lm.model <-

Hello. I'm trying to make k-step-ahead forecasts using ARFIMA(0, d, 0) models by taking the first T+k-1 coefficients in the binomial expansion of (1-B)^d, regarding (1-B)^d x(T+k) as an AR(T+k-1) on x(T+k), where x(T) is the series value at time T and k = 1, 2, 3, . That is, I forecast the series k values forward using the first T+k-1 coefficients in the binomial expansion of (1-B)^d as

Hello, In order to do a future forecast based on my past Time Series data sets (salespricesproduct1, salespricesproduct2, etc..), I used arima() functions with different parameter combinations which give the smallest AIC. I also used auto.arima() which finds the parameters with the smallest AICs. But unfortuanetly I could not get satisfactory forecast() results, even sometimes catastrophic

v1.0 of the forecasting bundle of packages is now on CRAN and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman

v1.0 of the forecasting bundle of packages is now on CRAN and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman

Hello everyone, I'm doing some benchmark comparing Arima [1] and SVR on time series data. I'm using an out-of-sample one-step-ahead prediction from Arima using the "fitted" method [2]. Do someone know how to have a two-steps-ahead forecast timeseries from Arima? Thanks, Matteo Bertini [1] http://robjhyndman.com/software/forecast [2] AirPassengers example on page 5

Dear R-Users, How can I compute simple moving averages from a time series in R? Note that I do not want to estimate a MA model, just compute the MA's given a lenght (as excel does). Thanks ________________________________________ Ricardo Gonçalves Silva, M. Sc. Apoio aos Processos de Modelagem Matemática Econometria & Inadimplência Serasa S.A. (11) - 6847-8889 ricardosilva@serasa.com.br

hello, I have a question about making n step ahead forecasts in cases where test and validation sets are availiable. For instance, I would like to make one step ahead forecasts on the WWWusage data so I hold out the last 10 observations as the validation set and fit an ARIMA model on the first 90 observations. I then use a for loop to sequentially add 9 of the holdout observations to make 1

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]]