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R代写 - ECON3389数据分析

时间：2020-12-06

Case Study and Course Project

Anatoly Arlashin

Boston College

Case Study: Agenda

Add a real life story to the theory of lectures and model estimation in R.

Broaden your understanding of the benefits of ML in economic/business applications.

Practice creating summary reports and video presentations for the course project.

ECON3389 ML in Economics | Fall’20 Lecture 09: Case Study and Course Project

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Case Study: Tips & Tricks

Case study is NOT simply a summary of an article/blog post. Instead, it should be

a review of the chosen case based on the agenda of our course, i.e. the use of ML in

Economics.

Think about your case as a presentation done in front of you, and try to come up

with any and all questions you might ask during such presentation.

I You will likely not find answers to those questions in the original article/blog post, but you

can try answering those questions yourself.

Of course, if your case involves some ML models/techniques that we have not yet

covered in our class, make sure to very briefly explain what those are.

ECON3389 ML in Economics | Fall’20 Lecture 09: Case Study and Course Project

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Course Project: Agenda

Course project is a full scale research project, and requires a substantial amount of

work to complete.

Unlike case study, project requires you to do all the work — find a dataset, for-

mulate research agenda, apply your knowledge of ML methods to build a reliable

inference/prediction model, and so on.

You will be tasked both with basic data analysis (summary statistics, visual plots),

statistical modeling (estimating a model using R), writing a research paper and cre-

ating a video presentation of your results.

Important: you should start working on the project as soon as possible, and keep

working on it on a regular basis. Rushing everything in the last couple of days will

likely produce inferior results.

ECON3389 ML in Economics | Fall’20 Lecture 09: Case Study and Course Project

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Course Project: Data

Each group can choose any dataset for their project. It could be one of the four

datasets I suggest or any other dataset. Unlike case study, there is no restriction on

how many groups are using the same dataset.

The four datasets available through Canvas are:

I Iowa liquor sales.

I US baseball and basketball salaries.

I Personal income and socio-demographic attributes.

You are free to use any other datasets, as long as it has at least 1000 observations

across at least 10 variables, but you do need to confirm the chosen dataset with me

first.

I If using Kaggle, make sure to not fall into a trap of repeating someone’s steps from one of

Kaggle’s challenges.

ECON3389 ML in Economics | Fall’20 Lecture 09: Case Study and Course Project

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Course Project: Research Question

Specifics of research questions depend on the nature of the data, but in general you

are required to do two things: build an inference/causal analysis model and build a

pure prediction model.

For both models you will need to choose the same outcome variable and use the rest

of the variables as your predictors (explanatory variables).

Inference model will likely be not too complicated — linear regression with a few

non-linear terms and/or interactions with factor variables.

Prediction model, however, can be as complex as you like — polynomial regression,

random forests, neural nets, etc.

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Course Project: Building a Model

Even for relatively simple linear models you will need to make educated decisions

about which variables and in which form to include in the model’s equation.

I If you have factor variables, than the most flexible approach will require interacting all factor

variables with all non-factor ones, which may lead to hundreds of regressors.

I On the other hand, for inference model there may not be any meaningful interpretation for

inclusion of all possible combinations, and thus you will have to balance additional regressors

vs ZCM vs interpretability.

Generally speaking, whenever choosing between competing models, you should always

use the train/test split of your data.

I This is especially important for pure prediction model, where overfitting could be a major

issue.

ECON3389 ML in Economics | Fall’20 Lecture 09: Case Study and Course Project

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Course Project: Tips & Tricks

Start working on course project ASAP. Anywhere between 50% and 80% of all the

work will be data management and analysis in R, and that is something prone to

being stuck with some issue for hours, if not days.

The earlier you start working, the more opportunities you will have to ask me ques-

tions/feedback.

Spread out the workload across all group members — one person can do general data

summary (tables, charts), another one work on best inference model and yet another

one on best prediction model.

Your video presentation should contain the bulk of your findings, but it is also some-

thing that I and your classmates will comment on, giving you a chance to fix any

spotted issues before submitting the final paper.

ECON3389 ML in Economics | Fall’20 Lecture 09: Case Study and Course Project

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