computeH {ConvergenceClubs}  R Documentation 
Compute H values
Description
Computes H values (crosssectional variance) according to the clustering algorithm by Phillips and Sul (2007, 2009)
Usage
computeH(X, quantity = "H", id)
Arguments
X 
matrix or dataframe containing data (preferably filtered, in order to remove business cycles) 
quantity 
string indicating the quantity that should be returned. The options are

id 
optional; row index of regions for which H values are to be computed; if missing, all regions are used 
Details
The cross sectional variation H_{it}
is computed as the quadratic
distance measure for the panel from the common limit and under the
hypothesis of the model should converge to zero as t tends towards infinity:
H_t = N^{1} \sum_{i=1}^N (h_{it}1)^2 \rightarrow 0 , \quad t\rightarrow \infty
where
h_{it} = \frac{\log y_{it}}{( N^{1} \sum_{i=1}^N log \, y_{it} )}
Value
A numeric vector, a matrix or a list, depending on the value of quantity
References
Phillips, P. C.; Sul, D., 2007. Transition modeling and econometric convergence tests. Econometrica 75 (6), 17711855.
Phillips, P. C.; Sul, D., 2009. Economic transition and growth. Journal of Applied Econometrics 24 (7), 11531185.
Examples
data("filteredGDP")
h < computeH(filteredGDP[,1], quantity="h")
H < computeH(filteredGDP[,1], quantity="H")
b < computeH(filteredGDP[,1], quantity="both")