Sauka ksauka

## Dynamic Fixed Effects Panel Data Model.ipynb

      
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                ksauka
                / Dynamic Fixed Effects Panel Data Model.ipynb
            
            
              Created
              December 22, 2021 07:03
                — forked from spillz/Dynamic Fixed Effects Panel Data Model.ipynb
            
              
                Demonstrate dynamic panel data estimation using IV in linearmodels
              
          
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## hierarchical_model.py
with pm.Model() as hierarchical_model:
    # global model parameters
    α_μ_tmp = pm.Normal('α_μ_tmp', mu=0., sd=100)
    α_σ_tmp = pm.HalfNormal('α_σ_tmp', 5.)
    β_μ = pm.Normal('β_μ', mu=0., sd=100)
    β_σ = pm.HalfNormal('β_σ', 5.)

    # train type specific model parameters
    α_tmp = pm.Normal('α_tmp', mu=α_μ_tmp, sd=α_σ_tmp, shape=n_train_types)
    # Intercept for each train type, distributed around train type mean
	with pm.Model() as hierarchical_model:
	# global model parameters
	α_μ_tmp = pm.Normal('α_μ_tmp', mu=0., sd=100)
	α_σ_tmp = pm.HalfNormal('α_σ_tmp', 5.)
	β_μ = pm.Normal('β_μ', mu=0., sd=100)
	β_σ = pm.HalfNormal('β_σ', 5.)

	# train type specific model parameters
	α_tmp = pm.Normal('α_tmp', mu=α_μ_tmp, sd=α_σ_tmp, shape=n_train_types)
	# Intercept for each train type, distributed around train type mean