联系方式

  • QQ:99515681
  • 邮箱:99515681@qq.com
  • 工作时间:8:00-23:00
  • 微信:codinghelp

您当前位置:首页 >> Algorithm 算法作业Algorithm 算法作业

日期:2019-11-26 10:38

Econ*3740 Introductory Econometrics

1.[10]  Obtain the data canadian_forest_fires_1950-2017.csv from Course Link. Using the steps in the R Tutorial Manual Section 8.4, regress the annual Number of Forest Fires on Year to estimate the trend and then construct a Breusch-Godfrey test statistic for autocorrelation of order 1 the long way. Then verify your answer using the bgtest() command (from the <lmtest> package). Submit your code and the value of the test statistic.


2.[10]  Using the steps in the R Tutorial section 8.7, plot the Number of Forest Fires per year against Year, and add a line showing the fitted values from Question 1. Is it possible that autocorrelation might be an indication of a mis-specified regression model? Explain.


3.[20] Now repeat the above steps, except regress the annual Number of  Fires on Year, Year2 and Year3. This is a cubic equation. If t denotes the year, your regression equation will be:


Report your results. Are the new regression coefficients significant?

Does the Breusch-Godfrey test still indicate serial correlation?


4.[20]  Following  the steps in the R Tutorial section 8.5, use the data in the file rts.xls to construct a Wu-Hausman test for exogeneity of the variable TEST using the variable SF (schooling of the father) as an instrument. Do it the long way using the fitted values from a regression to construct the instrument and computing a t test, then use the ivreg() command to check your answer. Explain how the t statistic in the first stage regression confirms the Wu-Hausman score from the ivreg() command.


版权所有:留学生编程辅导网 2020 All Rights Reserved 联系方式:QQ:99515681 微信:codinghelp 电子信箱:99515681@qq.com
免责声明:本站部分内容从网络整理而来,只供参考!如有版权问题可联系本站删除。 站长地图

python代写
微信客服:codinghelp