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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