How to simulate an AR(1) process with arima.sim and an estimated model?
r
Solution
It's not the clearest interface in the world, but the `model` argument is meant to be a list giving the ARMA order, not an actual `arima` model.
arima.sim(model=as.list(coef(model_AR)), n=100)
This will create a simulated series with AR coefficient .489 as estimated from your starting data. Note that the intercept is ignored.
Problem
I want to do the following two steps: - Based on a given time series, I want to calibrate an AR(1) process, i.e. I want to estimate the parameters. - Based on the estimated parameters, I want to simulate an AR(1) processes. Here was my approach: ``` set.seed(123) #Just generate random AR(1) time series; based on this, I want to estimate the parameters ts_AR <- arima.sim(n=10000, list(ar=c(0.5))) #1. Estimate parameters with arima() model_AR <- arima(ts_AR, order=c(1,0,0)) #Looks actually good model_AR Series: ts_AR ARIMA(1,0,0) with non-zero mean Coefficients: ar1 intercept 0.4891 -0.0044 s.e. 0.0087 0.0195 sigma^2 estimated as 0.9974: log likelihood=-14176.35 AIC=28358.69 AICc=28358.69 BIC=28380.32 #2. Simulate based on model arima.sim(model=model_AR, n = 100) Error in arima.sim(model = model_AR, n = 100) : 'ar' part of model is not stationary ``` I'm not the biggest time-series expert, but I'm pretty sure that an AR(1) process with a persistence parameter of below one should result in a stationary model. However, the error message tells me somethings different. So do I do something stupid here? If so, why and what should I do to simulate the AR(1) process based on my estimated parameters. Or can't you just pass the output of `arima` as the model input into `arima.sim`? Then, however, I don't understand how I get such an error message...I would expect something like "model input cannot be read. It should be something like ..."