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ConvergenceClubs (version 1.0.0)

findClubs: Finds convergence clubs

Description

Find convergence clubs by means of Phillips and Sul clustering procedure.

Usage

findClubs(X, dataCols, regions = NULL, refCol, time_trim = 1/3, cstar = 0,
  HACmethod = c("FQSB", "AQSB"))

Arguments

X

dataframe containing data (preferably filtered data in order to remove business cycles)

dataCols

integer vector with the column indices of the data

regions

integer scalar indicating, if present, the index of a column with codes of the regions

refCol

integer scalar indicating the index of the column to use for ordering data

time_trim

a numeric value between 0 and 1, representing the portion of time periods to trim when running log t regression model. Phillips and Sul (2007, 2009) suggest to discard the first third of the period.

cstar

numeric scalar, indicating the threshold value of the sieve criterion c to include units in the detected core (primary) group (step 3 of Phillips and Sul (2007, 2009) clustering algorithm). The default value is 0.

HACmethod

string indicating whether a Fixed Quadratic Spectral Bandwidth (HACmethod="FQSB") or an Adaptive Quadratic Spectral Bandwidth (HACmethod="AQSB") should be used for the truncation of the Quadratic Spectral kernel in estimating the log-t regression model with heteroskedasticity and autocorrelation consistent standard errors. The default method is "FQSB".

Value

Ad object of class convergence.clubs, containing 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 parameter regions is given)

Details

In order to investigate the presence of convergence clubs according to the Phillips and Sul clustering procedure, the following steps are implemented:

  1. (Cross section last observation ordering): Sort units in descending order according to the last panel observation of the period;

  2. (Core group formation): Run the log t regression for the first k units (2<k<N) maximizing k under the condition that t-value is >1.65. In other words, chose the core group size k* as follows:

    k=argmaxk{tk} subject to min{tk}>1.65

    If the condition tk>1.65 does not hold for k=2 (the first two units), drop the first unit and repeat the same procedure. If tk>1.65 does not hold for any units chosen, the whole panel diverges;

  3. (Sieve the data for club membership): After the core group is detected, run the logt regression for the core group adding (one by one) each unit that does not belong to the latter. If tk is greater than a critical value c add the new unit in the convergence club. All these units (those included in the core group k plus those added) form the first convergence club;

  4. (Recursion and stopping rule): If there are units for which the previous condition fails, gather all these units in one group and run the log-t test to see if the condition tk>1.65 holds. If the condition is satisfied, conclude that there are two convergence clubs. Otherwise, step 1 to 3 should be repeated on the same group to determine whether there are other subgroups that constitute convergence clubs. If no further convergence clubs are found (hence, no k in step 2 satisfies the condition tk>1.65), the remaining regions diverge.

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.

Andrews, D. W., 1991. Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica: Journal of the Econometric Society, 817-858.

See Also

mergeClubs, Merges a list of clubs created by findClubs;

mergeDivergent, Merges divergent units according to the algorithm proposed by von Lyncker and Thoennessen (2016)

Examples

Run this code
# NOT RUN {
data("countryGDP")

# }
# NOT RUN {
# Cluster Countries using GDP from year 2000 to year 2014
clubs <- findClubs(countryGDP, dataCols=2:35, regions = 1, refCol=35, time_trim = 1/3,
                   cstar = 0, HACmethod = "FQSB")
# }
# NOT RUN {
clubs <- findClubs(countryGDP, dataCols=2:35, regions = 1, refCol=35, time_trim = 1/3,
                   cstar = 0, HACmethod = "AQSB")
summary(clubs)


# }

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