Consider the following Python code:# 1.a first estimate model using NWS SE# dictionary with cov options# cov_opts = {'maxlags':24, # number of NW lags for SE#'use_correction':True} # small sample correction# reg_fit = smf.ols("LeadReal ~ 1", data=df).fit(cov_type='HAC', # cov type## 1.b first estimate model usingreg_fit = smf.ols("LeadReal ~ 1", data=df).fit()cov_kwds=cov_opts)# 2. extract info from reg objectreg_fit.paramsreg_fit.bse# extract only coeff# extract only SEreg_fit.summary() # print nice reg table with more infoa.b.C.d.This code produces HAC-adjusted standard errors as isThis code features a biased estimatorThe commented (#) part of this code specifies the options required for the HAC-adjustedstandard errorsAll of the above

In this question we have to understand about Let's understand and hope this helps, If you have any…

Consider the following Python code: # 1.a first estimate model using NWS SE # dictionary with cov options # cov_opts = {'maxlags':24, # number of NW lags for SE # 'use_correction': True} # small sample correction # reg_fit = smf.ols("LeadReal ~ 1", data=df).fit(cov_type='HAC', #cov type # cov_kwds=cov_opts) # 1.b first estimate model using reg_f __fit = smf.ols("LeadReal ~ 1", data=df).fit() # 2. extract info from reg object reg_fit.params # extract only coeff reg_fit.bse # extract only SE reg_fit.summary() # print nice reg table with more info a. b. This code produces HAC-adjusted standard errors as is This code features a biased estimator The commented (H) part of this code specifies the options required for tho HAC-adjusted

Consider the following Python code: # 1.a first estimate model using NWS SE # dictionary with cov options # cov_opts = {'maxlags':24, # number of NW lags for SE # 'use_correction': True} # small sample correction # reg_fit = smf.ols("LeadReal ~ 1", data=df).fit(cov_type='HAC', #cov type # cov_kwds=cov_opts) # 1.b first estimate model using reg_f __fit = smf.ols("LeadReal ~ 1", data=df).fit() # 2. extract info from reg object reg_fit.params # extract only coeff reg_fit.bse # extract only SE reg_fit.summary() # print nice reg table with more info a. b. This code produces HAC-adjusted standard errors as is This code features a biased estimator The commented (H) part of this code specifies the options required for tho HAC-adjusted

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

Consider the following Python code:
# 1.a first estimate model using NWS SE
# dictionary with cov options
# cov_opts = {'maxlags':24, # number of NW lags for SE
#
'use_correction':True} # small sample correction
# reg_fit = smf.ols("LeadReal ~ 1", data=df).fit(cov_type='HAC', # cov type
#
# 1.b first estimate model using
reg_fit = smf.ols("LeadReal ~ 1", data=df).fit()
cov_kwds=cov_opts)
# 2. extract info from reg object
reg_fit.params
reg_fit.bse
# extract only coeff
# extract only SE
reg_fit.summary() # print nice reg table with more info
a.
b.
C.
d.
This code produces HAC-adjusted standard errors as is
This code features a biased estimator
The commented (#) part of this code specifies the options required for the HAC-adjusted
standard errors
All of the above

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