Test for Poolability of Individual Data Series How do I test/verify if I can analyze my panel like dataset by simply pooling the individual series?
I have a dataset structured as a panel. Now I am wondering if I can simply
pool the individual series and estimate it via OLS or if I have to use another estimation technique.
(Any R hints and references are highly welcomed.)
 A: You can fit a hierarchical bayesian (HB) model without pooling and do an ordinary OLS by pooling the data and compare the models in terms of model fit, hold-out predictions etc to evaluate whether pooling outperforms the HB model. The model very briefly will look like so:
Model
$y_i \sim N(X\ \beta_i,\sigma^2\ I)$
$\beta_i \sim N(\bar{\beta},\Sigma)$
Priors
$\bar{\beta} \sim N(\bar{\bar{\beta}},\Sigma_0)$
$\Sigma \sim IW(R,d)$
$\sigma^2 \sim IG(sp,sc)$
While I do not use R, I do know that there are packages that will do the above for you. Someone more knowledgeable about R can perhaps help you out. 
A: Srikant is right.  The book you want is "Data Analysis Using Regression and Multilevel/Hierarchical Models" by Gelman and Hill, all the R code from the book, and the associated arm package in R.
A: The only further comment I would make is that the approach need not be Bayesian and the model need not be a mixed or random effects model. 
In the simplest case if you had two series in x the mean model may be:
y = b01 + I*b02 + b11*x + I.b12*x
Where I indicates a sample from the 2nd series. An omnibus F-test can be used to determine whether the additional parameters are required to maintain distinct series (Ho: b02 = b12 = 0). 
http://en.wikipedia.org/wiki/F_test
This can be extended to more series, but it soon becomes more efficient to use a mixed or random effects model. 
A: I think you are confusing the name of the tests.
A test for poolability of the dataset is basically a test to analyse the stability of the parameters (it can be performed across individuals and over time). In simple words, the goal of the test is to analyse if the same coefficients are applicable for all individuals and time. For this kind of analysis, the pooltest function in r can be used to test the stability of parameters across individuals.
If you need to ckeck if your dataset can be estimated as pooled OLS (ie. no panel effect), you need to verifify if the individual specific-effect (also called individual heterogeneity) is statistically different than zero. There are a lot of tests to analyse this but I normally use the Honda test. For more details, you can have a quick look in the section of tests for individual and time effects from the Baltagi's book - Econometric analysis of panel data.
A: The plm package provides a a function for the poolability test in just three steps:
# 1. Run a normal OLS model with fixed effects (model="within")
plm_model<- plm(y ~ x, data= dataset, model= "within"

# 2. Run a variable coefficients model with fixed effects (model="within")
pvcm_model<- pvcm(y ~ x, data= dataset, model= "within"

# Run the poolability test
pooltest(plm_model, pvcm_model)

The null hypothesis is that the dataset is poolable (i.e. individuals have the same slope coefficients), so if p<0.05 you reject the null and you need a variable coefficients model.
More info here
