讲解：Econometrics、R、RPython| Stati

Advanced Econometrics: Homework 3December 10, 2019Instructions:• Please form groups of three students. If you have trouble finding colleagues, write me ane-mail, and I will match you with others having the same problem.• Deadline for submissions is Friday, December 20, 2019, 23:59. Any late submissionswill be awarded zero points.• Your solution should have a form of Jupyter Notebook with R source-code. Code should beproperly commented, interpretations of results as well as theoretical derivations should bewritten in markdown cells. This is the only file you need to send. If you prefer not to writeformulas in LATEX, you can send PDF with your derivations and interpretations in additionalfile and R code in Jupyter Notebook.• Please, be concise, but remember to include and explain all important steps.• If you have any questions concerning the homework, do contact me by mail and we can setup a consultation. Do it rather sooner than later, I won’t give any consultation concerningthe homework after December 17.Problem 1:(2 points)Simulate 1000 data points from the linear modelyi = α + xiβ + �i,where x ∼ N(20, 9), � ∼ N(0, xγ). For each of model parameters, generate a single random valueyou will be using throughout the exercise, where α ∈ h0, 4i, β ∈ h0.5, 2i, γ ∈ h0, 3i.Remember to use set.seed() to make your results replicable. Recall that parameters of normaldistribution are in the form N(µ, σ2), not N(µ, σ).a) Estimate model y = xβ + � on the simulated data using OLS. Interpret the results. Do youexpect any of the OLS assumptions to be violated. If yes, make the corresponding tests, andinterpret the results.b) Reestimate the model using GLS. State which form the variance-covariance matrix Ω takesin your 代写Econometrics留学生作业、R编程语言作业代做、代写R课程设计作业 代写Python程序|代写留学生 Stacase. Also, please state the form of your weighting matrix Ω− 12 . Comment on theresults from GLS regression.1c) Estimate FGLS model for heteroscedasticity of the form σ2i = σ2xi (recall the food expenditureexample from seminar 8).d) In the OLS model, estimate standard errors using White heteroscedasticity consistent estimator.Compare White’s standard errors to those from OLS, GLS, and FGLS.Problem 2:(2 points)Please use the data in wages.csv to answer following questions. Estimate models based on speci-ficationLW AGEit = β0 + β1EXPit + β2EDit + β3SMSAit + β4F EMit + uit,where i is indicated by ID variable, t is indicated by Y EAR variable. The dependent variable isnatural logarithm of wage, EXP indicates working experience, ED indicates years of education,SMSA is a dummy variable for individuals living in urban areas, F EM is a dummy variableindicating female workers.Your task is to find out, whether the female variable is a significant determinant of wages. Pleaseuse the standard panel estimation methods (Pooled OLS, Fixed Effects, and Random effectsmodels), and perform all the necessary tests. For each estimator, state the conditions under whichit is valid.What are your conclusions? What additional estimation would you suggest?Problem 3:(1 point)Please use the data in wages.csv again, and create a subsample containing all individuals injust one year of your choice. Hence, you should estimate a model of following specification on across-sectional subsampleLW AGEi = β0 + β1EXPi + β2EDi + β3SMSAi + β4F EMi + ui.Test for the group specific heteroscedasticity based on the outcome of SMSA variable. Constructan FGLS estimator efficient in presence of such relationship.2转自：http://www.6daixie.com/contents/18/4580.html