mgao6767

Running          0.00 km ░░░░░░░░░░░░░░░░░░░  0.00/h
Cycling       2154.77 km ███████████████████ 29.09/h
Last month     519.12 km       1 achievement  18:33h

mgao6767

import sqlite3, sys, os, pathlib, logging
import pandas as pd

# fmt: off
states = [ 'AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA',
           'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME',
           'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM',
           'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX',
           'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY']
# fmt: on

mgao6767

/* This SAS program calculates the distance to default using the KMV-Merton model 
   with the iterated estimate of the volatility of firm value.  Many of the results 
   of Bharath and Shumway (2004) are generated by this program.  The program 
   requires the data described below, and it generates a permanent sas data file
   called ssd.kmv which contains distances to default.  The program calculates 
   monthly distances to default every year from 1980 to 1990 as it is currently written*/

/* This program requires two datasets: 

    ssd.comp,  which contains monthly observations taken from the quarterly compustat 

mgao6767

from multiprocessing.shared_memory import SharedMemory
from multiprocessing.managers import SharedMemoryManager
from concurrent.futures import ProcessPoolExecutor, as_completed
from multiprocessing import current_process, cpu_count, Process
from datetime import datetime
import numpy as np
import pandas as pd
import tracemalloc
import time

mgao6767

_                   =r"""A(W/2,*M(3*G
               *G*V(2*J%P),G,J,G)+((M((J-T
            )*V((G-S)%P),S,T,G)if(S@(G,J))if(
         W%2@(S,T)))if(W@(S,T);H=2**256;import&h
       ashlib&as&h,os,re,bi    nascii&as&k;J$:int(
     k.b2a_hex(W),16);C$:C    (W/    58)+[W%58]if(W@
    [];X=h.new("rip           em    d160");Y$:h.sha25
   6(W).digest();I$                 d=32:I(W/256,d-1)+
  chr(W%256)if(d>0@"";                  U$:J(k.a2b_base
 64(W));f=J(os.urando       m(64))        %(H-U("AUVRIxl

mgao6767

/* Use Jackknife method to compute discretionary accruals */
/* see https://mingze-gao.com/posts/compute-jackknife-coefficient-estimates-in-sas/ */

/* UseHribarCollinsTotalAccruals:
	- true:  use Hribar-Collins Cashflow Total Accruals 
	- false: use normal method */
%let UseHribarCollinsTotalAccruals = false;

/* Include %array and %do_over */
filename do_over url "https://mingze-gao.com/utils/do_over.sas";

mgao6767

Background

In certain scenarios, we want to estimate a model's parameters on the sample for each observation with itself excluded. This can be achieved by estimating the model repeatedly on the leave-one-out samples but is very inefficient. If we estimate the model on the full sample, however, the coefficient estimates will certainly be biased. Thankfully, we have the Jackknife method to correct for the bias, which produces the (Jackknifed) coefficient estimates for each observation.

Variable Definition

Let's start with some variable definitions to help with the explanation.

Variable	Definition

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/* ********************************************************************************* */
/* ******************** W R D S   R E S E A R C H   M A C R O S ******************** */
/* ********************************************************************************* */
/* WRDS Macro: INDRATIOS                                                             */
/* Summary   : Computes a broad range of financial ratios aggregated at              */
/*              the industry level using Fama-French industry classification         */
/* Date      : Apr, 2009                                                             */
/* Modified  : Nov, 2010                                                             */
/* Author    : Denys Glushkov, WRDS                                                  */
/* Parameters:                                                                       */

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/* ********************************************************************************* */
/* ******************** W R D S   R E S E A R C H   M A C R O S ******************** */
/* ********************************************************************************* */
/* WRDS Macro: INDCLASS                                                              */
/* Summary   : Constructs 4 different industry classifications based on SIC, NAICS,  */
/*             GICS and Fama-French industry classifications                         */
/*                                                                                   */
/* Date      : Feb, 2010                                                             */
/* Author    : Denys Glushkov, WRDS                                                  */
/* Variables :                                                                       */

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/* ********************************************************************************* */
/* ******************** W R D S   R E S E A R C H   M A C R O S ******************** */
/* ********************************************************************************* */
/* WRDS Macro: WINSORIZE                                                             */
/* Summary   : Winsorizes or Trims Outliers                                          */
/* Date      : April 14, 2009                                                        */
/* Author    : Rabih Moussawi, WRDS                                                  */
/* Variables : - INSET and OUTSET are input and output datasets                      */
/*             - SORTVAR: sort variable used in ranking                              */
/*             - VARS: variables to trim and winsorize                               */