BLMEstimator class
- class pytwoway.blm.BLMEstimator(params)
Bases:
object
Class for estimating BLM using multiple sets of starting values.
- Parameters
params (ParamsDict) – dictionary of parameters for BLM estimation. Run tw.blm_params().describe_all() for descriptions of all valid parameters.
- fit(jdata, sdata, n_init=20, n_best=5, ncore=1, rng=None)
Estimate BLM using multiple sets of starting values.
- Parameters
jdata (BipartitePandas DataFrame) – event study or collapsed event study format labor data for movers
sdata (BipartitePandas DataFrame) – event study or collapsed event study format labor data for stayers
n_init (int) – number of starting values
n_best (int) – take the n_best estimates with the highest likelihoods, and then take the estimate with the highest connectedness
ncore (int) – number of cores for multiprocessing
rng (np.random.Generator or None) – NumPy random number generator; None is equivalent to np.random.default_rng(None)
- plot_liks_connectedness(jitter=False, dpi=None)
Plot likelihoods vs connectedness for the estimations run.
- Parameters
jitter (bool) – if True, jitter points to prevent overlap
dpi (float or None) – dpi for plot
- plot_log_earnings(period='first', grid=True, dpi=None)
Plot log-earnings by worker-firm type pairs.
- Parameters
period (str) – ‘first’ plots log-earnings in the first period; ‘second’ plots log-earnings in the second period; ‘all’ plots the average over log-earnings in the first and second periods
grid (bool) – if True, plot grid
dpi (float or None) – dpi for plot
- plot_type_proportions(period='first', subset='all', dpi=None)
Plot proportions of worker types at each firm class.
- Parameters
period (str) – ‘first’ plots type proportions in the first period; ‘second’ plots type proportions in the second period; ‘all’ plots the average over type proportions in the first and second periods
subset (str) – ‘all’ plots a weighted average over movers and stayers; ‘movers’ plots movers; ‘stayers’ plots stayers
dpi (float or None) – dpi for plot