MCCExampleData: A Small MCC/DMC Example Data Set

In bayesMCClust: Mixtures-of-Experts Markov Chain Clustering and Dirichlet Multinomial Clustering

Description

A small MCC/DMC example data set – a small data set for demonstration purposes...

This small data set is from data from the Austrian Social Security Database (ASSD), which combines detailed longitudinal information on employment and earnings of all private sector workers in Austria since 1972. The IEW Working Paper Zweimueller et al. (2009) (see Source) gives an overview and a description of the main characteristics of the Austrian Social Security Database.

The ASSD was made available for the Austrian Center of Labor Economics and the Analysis of the Welfare State (http://www.labornrn.at/). This small sample consists of N=1000 male Austrian workers, who enter the labor market for the first time in the years 1975 to 1985 and are less than 25 years old at entry. The cohort analysis is based on an observation period from 1975 to 2005.

Usage

data(MCCExampleData)

Format

The format is:

 
List of 4
 $ Njk.i       : num [1:6, 1:6, 1:1000] 0 0 0 0 0 0 0 0 0 0 ...
  ..- attr(*, "dimnames")=List of 3
  .. ..$ : chr [1:6] "0" "1" "2" "3" ...
  .. ..$ : chr [1:6] "0" "1" "2" "3" ...
  .. ..$ : NULL
 $ initialState: num [1:1000] 4 1 4 3 0 1 2 1 4 2 ...
 $ obsList     :List of 1000
  ..$ SVNR1680347701: int [1:26] 4 4 5 5 5 5 5 5 5 5 ...
  ..$ SVNR1681207417: int [1:26] 1 1 0 0 0 0 0 2 0 0 ...
  ..$ SVNR1681671288: int [1:26] 4 0 0 1 0 5 5 5 5 5 ...
  .. [list output truncated]
 $ somePrior   :List of 5
  ..$ :List of 2
  .. ..$ xi : num [1:6, 1:6] 0.7303 0.1521 0.0901 0.0589 0.0435 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:6] "0" "1" "2" "3" ...
  .. .. .. ..$ : chr [1:6] "0" "1" "2" "3" ...
  .. ..$ eta: num 1
  ..$ :List of 2
  .. ..$ eta: num [1:2] 0.632 0.368
  .. ..$ xi : num [1:6, 1:6, 1:2] 0.2163 0.1072 0.0576 0.0373 0.0286 ...
  ..$ :List of 2
  .. ..$ eta: num [1:3] 0.243 0.258 0.5
  .. ..$ xi : num [1:6, 1:6, 1:3] 0.5075 0.2408 0.1595 0.1048 0.0744 ...
  ..$ :List of 2
  .. ..$ eta: num [1:4] 0.193 0.221 0.238 0.348
  .. ..$ xi : num [1:6, 1:6, 1:4] 0.556 0.245 0.196 0.136 0.1 ...
  ..$ :List of 2
  .. ..$ eta: num [1:5] 0.246 0.232 0.156 0.143 0.223
  .. ..$ xi : num [1:6, 1:6, 1:5] 0.2104 0.1581 0.0665 0.0414 0.0388 ...
 

Details

MCCExampleData is a list containing the following objects:

Njk.i

A 3-dimensional array of dimension 6x6x1000 containing the transition frequencies (6 x 6-matrices) of 1000 individuals. These represent the counts of transitions between wage categories from year to year with varying observation periods. Categories 1 to 5 correspond to the wage quintiles and 0 to no income.

initialState

A vector giving the initial wage category for 1000 individuals.

obsList

A list of 1000 numeric vectors (of integers with variable lengths) representing wage categories. Wage mobility time series with variable lengths describing (transitions between) wage categories (from year to year) of 1000 individuals where categories 1 to 5 correspond to the wage quintiles (in the income distribution of the corresponding year) and 0 to no income. Each positive number represents the position in the income distribution in terms of quintiles of a particular year.

somePrior

A list of lists each containing prior-parameters for the group sizes and transition probabilities where the (index) number of the list corresponds to the number of clusters/groups.

Note

Note, that in contrast to the literature (see References), the numbering (labelling) of the states of the categorical outcome variable (time series) in this package is sometimes 0,...,K (instead of 1,...,K), however, there are K+1 categories (states)!

Source

The following IEW Working Paper gives an overview and a description of the main characteristics of the Austrian Social Security Database:

Zweimueller, Josef, Winter-Ebmer, Rudolf, Lalive, Rafael, Kuhn, Andreas, Wuellrich, Jean-Philippe, Ruf, Oliver and Buechi, Simon, Austrian Social Security Database (May 4, 2009). Available at SSRN: http://ssrn.com/abstract=1399350 or at http://www.labornrn.at/wp/wp0903.pdf.

References

Sylvia Fruehwirth-Schnatter, Christoph Pamminger, Andrea Weber and Rudolf Winter-Ebmer, (2011), "Labor market entry and earnings dynamics: Bayesian inference using mixtures-of-experts Markov chain clustering". Journal of Applied Econometrics. DOI: 10.1002/jae.1249 http://onlinelibrary.wiley.com/doi/10.1002/jae.1249/abstract

Christoph Pamminger and Sylvia Fruehwirth-Schnatter, (2010), "Model-based Clustering of Categorical Time Series". Bayesian Analysis, Vol. 5, No. 2, pp. 345-368. DOI: 10.1214/10-BA606 http://ba.stat.cmu.edu/journal/2010/vol05/issue02/pamminger.pdf

See Also

LMEntryPaperData, MCCExtExampleData, mcClust, dmClust

Examples

data(MCCExampleData)
str(MCCExampleData) 

# see example(s) in mcClust and dmClust

bayesMCClust documentation built on May 29, 2017, 3:31 p.m.