computeH  R Documentation 
Computes H values (crosssectional variance) according to the clustering algorithm by Phillips and Sul (2007, 2009)
computeH(X, quantity = "H", id)
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 
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) = 1/N ∑ [h(it)1]^2 > 0 as t > infinity
where
h(it) = N log[y(it)] / ∑ log[y(it)]
A numeric vector, a matrix or a list, depending on the value of quantity
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.
data("filteredGDP") h < computeH(filteredGDP[,1], quantity="h") H < computeH(filteredGDP[,1], quantity="H") b < computeH(filteredGDP[,1], quantity="both")
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