R/computeH.R

In ConvergenceClubs: Finding Convergence Clubs

#' Compute H values
#'
#' Computes H values (cross-sectional variance) according to the clustering
#' algorithm by Phillips and Sul (2007, 2009)
#'
#' @param X matrix or dataframe containing data (preferably filtered, in order to remove business cycles)
#' @param quantity string indicating the quantity that should be returned. The options are
#' \code{"H"}, the default, only the vector of cross-sectional variance is returned; \code{"h"},
#' only the matrix of transition path h is return; \code{"both"}, a list containing
#' both h and H is returned.
#' @param id optional; row index of regions for which H values are to be computed;
#' if missing, all regions are used
#'
#'
#' @return A numeric vector, a matrix or a list, depending on the value of \code{quantity}
#'
#'
#' @details The cross sectional variation \eqn{H_{it}}{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 \emph{t} tends towards infinity:
#'              \deqn{H_t = N^{-1} \sum_{i=1}^N (h_{it}-1)^2 \rightarrow 0 , \quad   t\rightarrow \infty}{
#'                   H(t) = 1/N \sum [h(it)-1]^2 --> 0  	as   t -> infinity  }
#'          where
#'              \deqn{h_{it} = \frac{\log y_{it}}{( N^{-1} \sum_{i=1}^N log \, y_{it} )} }{
#'                   h(it) = N log[y(it)] / \sum log[y(it)]    }
#'
#'
#' @references
#' 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.
#'
#'
#' @examples
#' data("filteredGDP")
#'
#' h <- computeH(filteredGDP[,-1], quantity="h")
#' H <- computeH(filteredGDP[,-1], quantity="H")
#' b <- computeH(filteredGDP[,-1], quantity="both")
#'
#'
#' @export



computeH <- function(X, quantity="H", id){

    quantity <- match.arg( quantity, c("h", "H", "both") )

    if( missing(id) ){
        xx <- X
    }else xx <- X[id, ]

    h <- apply(xx, 2, function(x) x/mean(x) )

    if( identical( quantity, 'h') ){
        return(h)
    }else{
        H <- colMeans( (h-1)^2 )
        if( identical(quantity, 'H') ){
            return(H)
        }else return( list(h=h, H=H) )
    }
}