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