search for: armax

Pro, I am PhD student , i am usuing Rational Expectations Model, I want to model different timeseries with ARMAX models in R because I think that ARMAX models will map best to these data. I want know the steps to use R for ARMAX models I coudn't find any solutions in the R help and therefore I want to ask all of you. Does anyone know how to solve this problem??? That would be great! Thanks a lot f...

Hi, I want to model different timeseries with ARMAX models in R because I think that ARMAX models will map best to these data. Besides I don't want to use the order of the AR or MA part but the lag e.g. AR Part =ar1, ar2, ar7; MA Part =ma1, ma3 and I want to use exogenous variables as well. I coudn't find any solutions in the R help and the...

Hello all, I''m having fun again with the arima function. This time I read in: http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm <<It has recently been suggested (by a reliable source) that using xreg in arima() does NOT fit an ARMAX model [insert slap head icon here]. This will be investigated as soon as time permits.>> (by R.H. Shumway & D.S. Stoffer) This is quite surprising... Does anybody know anything about it? Marc Vinyes (AleaSoft) [[alternative HTML version deleted]]

I am trying to understand how to fit an ARMAX model with the arima function from the stats package. I tried the simple data below, where the time series (vector x) is generated by filtering a step function (vector u, the exogenous signal) through a lowpass filter with AR coefficient equal to 0.8. The input gain is 0.3 and there is a 0.01 nor...

...0.613926 Adjusted R-squared 0.040436 S.D. dependent var 12.36165 S.E. of regression 12.10914 Akaike info criterion 7.831584 Sum squared resid 75368.51 Schwarz criterion 7.856235 Log likelihood -2021.465 Durbin-Watson stat 1.969820 Inverted MA Roots .53 -.48 gretl: Model 13: ARMAX estimates using the 517 observations 2-518 Estimated using Kalman filter (exact ML) Dependent variable: (1-L) Spot Standard errors based on Outer Products matrix VARIABLE COEFFICIENT STDERROR T STAT P-VALUE theta_1 -0.0491101 0.0439294 -1.118 0...

Dear R-help-archive.. I am trying to figure out how to make arima prediction when I have a process involving multivariate time series input, and one output time series (output is to be predicted) .. (thus strictly speaking its an ARMAX process). I know that the arima function of R was not designed to handle multivariate analysis (there is dse but it doesnt handle arma multivariate analysis, only simulations). But there is this beautiful "xreg" as parameter for arima and I was wondering.. for the case of one output seri...

Hello, I try to simulate an ARMA-model using R, but I didn't find any function to generate such timeseries. In Splus there is the function arima.sim which generates AR-, MA- and ARIMA-series. Is there any similar in R? Best regards, Frank Beimfohr -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

...0, 2008 at 10:36 AM, Jose Capco wrote: > Dear R-help-archive.. > > I am trying to figure out how to make arima prediction when I have a > process involving multivariate time series input, and one output time > series (output is to be predicted) .. (thus strictly speaking its an > ARMAX process). I know that the arima function of R was not designed > to handle multivariate analysis (there is dse but it doesnt handle > arma multivariate analysis, only simulations). But there is this > beautiful "xreg" as parameter for arima and I was wondering.. > for the cas...

Dear All, I have a short question. Is there any readily available function that could generate either an ARMAX model or, more generally, an AutoRegressive Distributed Lag model? I am looking for a function that is similar to armaSim() function in fArma package. Thank you. MP

Hi I'm trying to measure the effect of a policy intervention (Box and Tiao, 1975). This query has to do with the coding of the model rather than with the particulars of my dataset, so I'm not providing the actual dataset (or a simulated one) in this case, apart from some general description. The time series are of length n=34 (annual observations between 1977 and 2010). The policy