Merges divergent units according the algorithm proposed by von Lyncker and Thoennessen (2016)
mergeDivergent(clubs, time_trim, threshold = -1.65)
an object of class convergence.clubs
(created by findClub
or mergeClubs
function)
a numeric value between 0 and 1, representing the portion of
time periods to trim when running log t regression model; if omitted, the same
value used for clubs
is used.
a numeric value indicating the threshold to be used with the t-test.
A list of Convergence Clubs, for each club a list is return with the
following objects: id
, a vector containing the row indices
of the regions in the club; model
, a list containing information
about the model used to run the t-test on the regions in the club;
regions
, a vector containing the names of the regions of the club (optional,
only included if it is present in the clubs
object given in input).
von Lyncker and Thoennessen (2016) claim that units identified as divergent by the basic clustering procedure by Phillips and Sul might not necessarily still diverge in the case of new convergence clubs detected with the club merging algorithm. To test if divergent regions may be included in one of the new convergence clubs, they propose the following algorithm:
Run a log t-test for all diverging regions, and if
Run a log t-test for each diverging regions and each club, creating a
matrix of t-values with dimensions
Take the highest
The algorithm stops when no t-value > e* is found in step 3, and as a consequence all remaining regions are considered divergent.
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.
von Lyncker, K.; Thoennessen, R., 2016. Regional club convergence in the EU: evidence from a panel data analysis. Empirical Economics, doi:10.1007/s00181-016-1096-2, 1-29.
mergeClubs
, Merges a list of clubs created by findClubs
;
mergeDivergent
, merges divergent units according to the algorithm proposed by von Lyncker and Thoennessen (2016).
# NOT RUN {
data("countryGDP")
#Cluster Countries using GDP from year 1970 to year 2003
clubs <- findClubs(countryGDP, dataCols=2:35, regions = 1, refCol=35,
time_trim = 1/3, cstar = 0, HACmethod = "AQSB")
summary(clubs)
# Merge clusters and divergent regions
mclubs <- mergeClubs(clubs, mergeDivergent=TRUE)
summary(mclubs)
# }
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