cty time tl
Argentina 2009_Q4 3
Argentina 2010_Q1 2
Argentina 2010_Q2 7
Argentina 2010_Q3 7
Argentina 2010_Q4 10
Argentina 2011_Q1 7
Argentina 2011_Q2 7
Argentina 2011_Q3 1
Argentina 2011_Q4 7
Argentina 2012_Q1 5
The data set has around 40 countries with each country having quarterly data for 5 years. As I am new to R, can someone assist me with how to perform a cross sectional time series ARIMA/ARMA models etc. and forecast in R? Some basic codes to prepare the data, to perform analysis and forecast for this series as an example would be helpful.
and 
and 
. Since anomalies are present the appropriate regression needs to take into account these effects. Following are the three models ( including any necessary lag structures in the two inputs ) and the appropriate ARIMA structure obtained from an automatic transfer function run using AUTOBOX ( a piece of software I have been developing for the last 42 years )
and 
and 
. We now take the three cleansed series returned from the modelling process and estimate a minimally sufficient common model which in this case would be a comtemporary and 1 lag PDL on tweets and a contemporary PDL on wiki with an ARIMA of (1,0,0)(0,0,0). Estimating this model locally and globally provides insight as to the commonality of coefficients .
with coefficients 
. The test for commonality is easily rejected with an F value of 79 with 3,291 df. Note that the DW statistic is 2.63 from the composite analysis. The summary of coefffici
ents is presented here. The OP poster reflected that the only software he has access to is insufficient to be able to answer this thorny research question.
Best Answer
I had a similar question and the answers there were quite helpful. You might want to read this.
I would summarise it here for you. If your dataset is called
dfand you want to forecast for each of the 40 countries separately, then you can do so by using either of the packages-dplyrordata.tablealong withforecast:library(dplyr) library(forecast) model_fits <- group_by(df, cty) %>% do(fit=auto.arima(.$tl))OR
library(data.table) library(forecast) temp <- setDT(temp)[, list(AR = list(auto.arima(tl))), by = cty]