ECON 322: Econometric Analysis 1Final data project: Winter 2019General instructionsThis last assignment is due on Sunday April 7 before 11:30pm on Learn. It is a small research projectand you will be evaluated on your ability to correctly use the different concepts covered during theterm. It is approximately worth 3 assignments (8.5 points out of 25). The drop box will not close, soyou will be allowed to submit the project late. The rule for late submissions is as follows: if you arebetween 0.01 and 60 minutes late you get a 2/10 penalty, between 60.01 and 120 minutes late you get5/10 penalty, and you get a 10/10 penalty if you submit the project more than two hours late. Toavoid penalties, do not wait at the last minute to upload it. Notice that there is no justification fornot submitting this assignment (I remind you that doctor notes for being sick around the due date isnot accepted for assignments).For the final assignment, I want it to be organized like a report. I want the codes and R output alongwith the comments and discussion in the same pdf file. If you upload your document in any otherformat (.doc, .docx, ...), I will not mark it. If you want to see what I expect from you, download thedocument Assignment7 W17Sol.pdf that I uploaded in the folder “Final Project”. It is the suggestedsolution from Winter 2017 project. If you only put the codes and output with no discussion (onesentence it not considered a discussion), you get 0 out of 10 points. To obtain the full mark, you needto justify what you are doing (choice of the model, tests, etc.) analyze the results (interpretation ofthe coefficients, discussion about the validity of your results, etc.), and show me that you know howto use the different concepts used in class. The more concepts you use the higher will be your mark.The projectFor this project, we use the data “Fatalities” from the “AER” package. To get the data, install the“AER” package, then load the data using the following command:library(AER)data(Fatalities)The data frame will be called “Fatalities”. For a complete description of the variables, you can usethe help() function as follows:help(Fatalities)The main objective of the project is to measure the impact of different alcohol policies on carfatalities. As you will discover by looking at the help file, there are several measures of car fatalities.However, we assume that the same model can be used to explain any of the measure. You can thereforeselect the model independently from which car fatality measures is used on the left hand side. Youmay want to comment on that assumption. Do you think it is a reasonable assumption?You will see that all qualitative variables are expressed as factors. We have covered that in one ofyour tutorials, so you should be able to figure out how to deal with such format. For example, you canrun a regression of f atal on jail, which is “yes” or “no”, and let R create dummy variables for you:Econ 322 Final Project Page 1 of 5lm(fatal~jail, Fatalities)#### Call:## lm(formula = fatal ~ jail, data = Fatalities)#### Coefficients:## (Intercept) jailyes## 1034.4 -424.4The main policy variable we are interested in is beertax, drinkage, breath and jail. breath is equalto “yes” if the police is authorized to administer pre-arrest breath test for alcohol and jail is equal to“yes” if the law requires a jail sentence for the first conviction.Keep in mind that the objective is to test whether the different policies have an impact on carfatalities.Part IYou will see that the dataset is a panel of 48 states and 7 years (1982 to 1988). We have not seen howto deal with panel data, but we will ignore it and proceed as if we had cross-sectional data. In Part 2,I will show you one way to estimate such models.The objective of this part is to build a model that will allow you to test the effect of the differentpolicies on different types of car fatalities. For now, we assume that time and state have no impact onthe results. In other words, we assume that there are no unobserved differences across time and statesthat may be related to the policy adoptions and on car fatalities. You may want to add a paragraphto discuss this assumption and how it may impact your results.The selection of the variables you want to control for must be based only on your economic intuition.You need to control for variables that are likely to be correlated with the number of car accidents and,at the same time, with the choice of adopting the policy. You may also consider to add variables thatyou think may be related to the number of accidents, even if they are unrelated to the policy adoptionby the states. The addition of such variables, as you should know, is likely to reduce the standarderror of the coefficients.You may begin by assuming that the different policies are correlated with each others, so the modelshould include the three policy indicators. The smallest model to consider is therefore:lm(fatal~jail+breath+beertax, Fatalities)#### Call:## lm(formula = fatal ~ jail + breath + beertax, data = Fatalities)#### Coefficients:## (Intercept) jailyes breathyes beertax## 1222.97 -572.14 -383.41 58.92Of course, it would be unacceptable for you to choose this simple model. Once you have decided onwhich variable to include, you have to think about how to insert those variables in your model (in log,with a squared term, with interactions etc.). You may want to apply some tests that we saw in classto select one model among a few that you want to consider. You may want to consider interacting theEcon 322 Final Project Page 2 of 5policy variables. It is possible, for example, that the effect of the beer tax is different in states wherejail = yes compared to those where jail = no.To build your model (functional form and variable selection), use the total number of car fatalities(the variable f atal). The following is a todo list. These are not parts that you have to answerindividually. These are just elements that I expect to find somewhere in your report. Discussion on which variable should be included and why. Discussion on how each variable should enter the model (in log, with interactions, squared, etc.).It may not be obvious for all variables, but try your best. Estimate some models (if you have more than one in mind... well, you should certainly try morethan one) Test for correct specification (Chapter 9), homoscedasticity (Chapter 8). It is very importantthat you only use robust tests if you reject homoscedasticity. You will be penalized if you don’t. Any other things to look for before going to the interpretation part (are there any outliers)? Interpret the result and discuss the possible weakness of the model. Here I want the interpretationof all coefficients of the policy variables (and their interactions if any). We are not interested inthe coefficients of the control variables. Estimate the same model (same right hand sides) for a few car fatality measures. Choose 3measures that you are interested to analyze among the 10 available in the dataset. Interpret theresults.There is no such thing as finding the right model. The evaluation is only based on how you justifyyour choice and how well you use the different concepts that we have covered during the term.Part IIWe saw in class that past information can be used as a proxy for state characteristics that may havepushed them to adopt the policy. As an exercise, estimate your model (the same used in Part I) for1985 only with f atal as dependent variabe, and add the 1982 f atal as a proxy. Compare the resultswith and without the proxy. You have to estimate the model using this dataset:dat1985 and create the proxy as follows:fatal1982 Compare also your results with what you obtained in Part I. What do you think is the weaknessof this approach.Econ 322 Final Project Page 3 of 5Part IIIIn the final part of the project, I want you to estimate a fixed-effect model. It looks like a fancy word,but it is just a regression with many dummy variables. When we deal with panel data, we want tocontrol for time trend and unobserved heterogeneities across states. If we don’t, we are likely to getbiased estimator of the policy effect, for the same reason we would bias the estimate if we do notcontrol for observed variables that are related to accidents and policy adoption. Adding time andstate fixed effect means that we add dummy variables for states and years. We have 7 years and 48states, so we want 6 time dummies, and 47 state dummies (we omit one to avoid multicolinearity).For example, the NewYork dummy is 1 if the observation is from the New York state and 0 otherwise.Similarly, the 1982 dummy is 1 of the observation is from 1982 and 0 otherwise.In R, it is easy to add time and year fixed effects. All you need is for these variables to be definedas factors. You do not want year to be defined as integer because lm() would think that it is a regularvector of numbers. We can check the type as follows:is(Fatalities$year, factor)## [1] TRUEis(Fatalities$state, factor)## [1] TRUEThey are factors, so we are good to go. All you have to do is to add year and state to your modelfrom part I. For example:res length(coef(res))## [1] 55You see that there are 55 coefficients; an intercept, the coefficient of jail and 53 coefficients forthe time and state dummies. When you print the results, you never report the coefficients of the timeand state dummy variables. Here is how to use stagazer and omit all coefficients of the time and statefixed effects in the printed results:library(stargazer)stargazer(res, type=text, omit=c(year,state), digits=5)#### ===============================================## Dependent variable:## ---------------------------## fatal## -----------------------------------------------## jailyes 0.45451## (36.26195)#### Constant 952.64660***## (38.31339)#### -----------------------------------------------## Observations 335## R2 0.99068## Adjusted R2 0.98888Econ 322 Final Project Page 4 of 5## Residual Std. Error 95.20186 (df = 280)## F Statistic 551.20140*** (df = 54; 280)## ===============================================## Note: *pYou get the info about the number of variables in the first degrees of freedom of the F-Statistic.Estimate your model of Part I with the year and state fixed effects added to it. Do it for all3 measures of car fatalities you analyzed. Interpret your results and compare them with what youobtained in Part I.Econ 322 Final Project Page 5 of 5本团队核心人员组成主要包括BAT一线工程师，精通德英语！我们主要业务范围是代做编程大作业、课程设计等等。我们的方向领域：window编程 数值算法 AI人工智能 金融统计 计量分析 大数据 网络编程 WEB编程 通讯编程 游戏编程多媒体linux 外挂编程 程序API图像处理 嵌入式/单片机 数据库编程 控制台 进程与线程 网络安全 汇编语言 硬件编程 软件设计 工程标准规等。其中代写编程、代写程序、代写留学生程序作业语言或工具包括但不限于以下范围:C/C++/C#代写Java代写IT代写Python代写辅导编程作业Matlab代写Haskell代写Processing代写Linux环境搭建Rust代写Data Structure Assginment 数据结构代写MIPS代写Machine Learning 作业 代写Oracle/SQL/PostgreSQL/Pig 数据库代写/代做/辅导Web开发、网站开发、网站作业ASP.NET网站开发Finance Insurace Statistics统计、回归、迭代Prolog代写Computer Computational method代做因为专业，所以值得信赖。如有需要，请加QQ：99515681 或邮箱：99515681@qq.com 微信：codehelp
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