[Python] multiprocess 패키지를 이용한 병렬처리
import time import os from multiprocess import Pool import multiprocess as mp
Great thanks to Professor Yoon at KAIST
Chapter | Keywords |
---|---|
1-1. Panel Data & Error Component Model |
#Pooled_Regression #Error_Component_Model |
1-2. Fixed Effect | #Within #Differenced #Between #Dummy_Variable_Regression #Hausman_test |
2. Binary Choice | #Probit #Logit |
3. Multiple Choice | #Multinomial_logit #Conditional_logit #IIA #Nested_logit #Mixed_logit #Probit #Ordered_Response |
4. Censoring and Selection | #Censoring #Tobit #Selection #Heckman's_model |
5-1. Treatment Effects and Regression | #Rubin's_causal_model #ATE #ATT #Missing_Data_Problem #Selection_bias #Endogeneity #Randomized_experiment #Unconfoundness |
5-2. Conditional Expectation Function | #regression_anatomy #CIA #OVB |
6. Matching | #strong_ignorability #regression_adjustment #IPW #counterfactual #nnmatch #PSM |
7-1. Instrumental Variables - Constant Effect |
#IV #2SLS #ILS #Wald_estimator #weak_instrument |
7-2. Instrumental Variables - Hetergenous Effect |
#external_validity #LATE #Exclusion_restriction #Monotonicity #complier #defier |
8. Differences-in-Differences | #Common_trend_assumption #Compound_effect #Ashenfelter's_dip #DDD |
9. Regression Discontinuity | #Sharp_RD #Fuzzy_RD #Validity_issue |
import time import os from multiprocess import Pool import multiprocess as mp
#Sharp_RD #Fuzzy_RD #Validity_issue
#Common_trend_assumption #Compound_effect #Ashenfelter’s_dip #DDD
일반 정의
#external_validity #LATE #Exclusion_restriction #Monotonicity #complier #defier