5. 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_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. This code produces HAC-adjusted standard errors as is b. This code features a biased estimator justed errors d. All of the above