Functions to compute the grouping on firms
grouping.getMeasures()
Extract the measurement matrix to be given to the classification algorithm
grouping.classify()
clusters firms based on their cross-sectional wage distributions
grouping.classify.once()
grouping.append()
Append result of a grouping to a data-set
Functions to estimate the interacted model using the clustered data.
m2.mini.new()
Model creation
m2.mini.simulate()
simulate a dataset according to the model
m2.miniql.estimate()
estimates interacted model using quasi-linkelood on the data moments
m2.mini.estimate()
estimates interacted model
m2.mini.plotw()
plotting mini model
m2.mini.vdec()
Computes the variance decomposition by simulation
m2.mini.impute.movers()
imputes data for movers according to the model. The results are stored in k_imp, y1_imp, y2_imp
m2.mini.impute.stayers()
imputes data according to the model
Functions to estimate the mixture model using the clustered data.
m2.mixt.new()
create a random model for EM with endogenous mobility with multinomial pr
m2.mixt.simulate.sim()
Simulates data (movers and stayers) and attached firms ids. Firms have all same expected size.
em.control()
Create a control structure for running EM algorithms
m2.mixt.estimate.all()
Estimates the static mixture model on 2 periods
m2.mixt.vdec()
m2.mixt.wplot()
plots the wages of a model
m2.mixt.pplot()
plots the proportions of a model