similar to: using predict.lm function

Good day, I'm using a VAR model to forecast sales with some extra variables (google trends data). I have divided my dataset into a trainingset (weekly sales + vars in 2006 and 2007) and a holdout set (2008). It is unclear to me how I should predict the out-of-sample data, because using the predict() function in the vars package seems to estimate my google trends vars as well. However, I want

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 <-

I have a dynamic time series model like this: dyn$lm( y ~ lag(y,-1) + x + lag(x,-1)+lag(x,-2) ) I need to do an out of sample forecast with this model. Is there any way I can do this with R? It would be greatly appreciated if some one can give me an example. Thanks. Harry [[alternative HTML version deleted]]

hello, I am trying to use predict.lm to make point forecasts based on a model with continuous and categorical independent variables I have no problems fitting the model using lm, but when I try to use predict to make point predictions. it reverts back to the original dataframe and gives me the point predictions for the fitted data rather than for the new data, I imagine that I am missing

I'm trying to do the following out of sample regression with autoregressive terms and additional x variables: y(t+1)=const+B(L)*y(t)+C(1)*x_1(t)...+C(K)*x_K(t) where: B(L) = lag polynom. for AR terms C(1..K) = are the coeffs. on K exogenous variables that have only 1 lag Question 1: ----------- Suppose I use arima to fit the model:

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

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).

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.

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]

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]]

Hello I am trying to use predict.lm, but I am having trouble getting out of sample predictions. I am getting the same output if I use the following three commands: predict(ModeloLineal,predictors[721:768,]) predict(ModeloLineal,predictors[1:768,]) predict(ModeloLineal) where ModeloLineal is the output from ModeloLineal<-lm(dataTS[,6] ~ predictors[1:720,]), so the first 720 observations of

> 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

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

(My apologies if this is a repeated posting. I couldn't find any trace of my previous attempt in the archive.) I'm having trouble with forecast() in the dse2 package. It works fine for me on a model without a trend, but gives me NaN output for the forecast values when using a model with a trend. An example: # Set inputs and outputs for the ARMA model fit and test periods

Hello R users, Hope everyone is doing great. I have a dataset that is in .csv format and consists of two columns: one named Period (which contains dates in the format yyyy_mm) and goes from 1995_10 to 2007_09 and the second column named pcumsdry which is a volumetric measure and has been formatted as numeric without any commas or decimals. I imported the dataset as pauldataset and made use of

Hi, I'm trying to model an ARMA(1,[1,4]), i.e. I want only lags 1 and 4 of the Moving Average part. It's the '[1,4]' part that is giving me a problem. I've tried different arma's and arima's in different packages, namely: packages tseries, fArma, FinTS, timeSeries, TSA, Zelig, ds1, forecast For example, with package FinTS: > ( ARIMA(y, order=c(1,0,c(1,4))) )

Hi, I’m having mayor issues with predict from the nnet package. I’m training a neural network for forecasting. I trained the network with info from 1995 to 2009 and I want to forecast month by month 2010.(the network forecasts one month at a time). Since I have to do iterative forecasting, im using predict several times including, including the new forecast each time, but for some reason

Good afternoon! I'm trying to model a time series on the following data, which represent a monthly consumption of juices: >x<-scan() 1: 2859 3613 3930 5193 4523 3226 4280 3436 3235 3379 3517 6022 13: 4465 4604 5441 6575 6092 6607 6390 6150 6488 5912 6228 10196 25: 7612 7270 8617 9535 8449 8520 9148 8077 7824 7991 7660 12130 37: 9135 9512 9631 12642

Hello, I am using the predict function for VAR in r obtaining the following object for the predictions with the following command PronFac <- predict(VARFactores,n.ahead=1) > PronFactores$fcst $PC1 fcst lower upper CI PC1.fcst 2.284497 -0.8033048 5.3723 3.087802 $PC2 fcst lower upper CI PC2.fcst -0.938333 -4.346927 2.470261 3.408594 $PC3

I am new to R and new to time series modeling. I have a set of variables (var1, var2, var3, var4, var5) for which I have historical yearly data. I am trying to use this data to produce a prediction of var1, 3 years into the future. I have a few basic questions: 1) I am able to read in my data, and convert it to a time series format using 'ts.' data_ts <- ts(data, start = 1988, end =