Computes H values (cross sectional variance) according to the clustering algorithm by Phillips and Sul (2007, 2009)
computeH(X, id)matrix or dataframe containing data (preferably filtered, in order to remove business cycles)
optional; row index of regions for which H values are to be computed; if missing, all regions are used
A numeric vector
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 $$h_{it} = \frac{\log y_{it}}{( N^{-1} \sum_{i=1}^N log \, y_{it} )} $$
Phillips, P. C.; Sul, D., 2007. Transition modeling and econometric convergence tests. Econometrica 75 (6), 1771-1855.
Phillips, P. C.; Sul, D., 2009. Economic transition and growth. Journal of Applied Econometrics 24 (7), 1153-1185.