Econometrics DATA REGRESSION on STATA

I have an econometrics project. Here is how it is: How does level of income impact credit card debt in the US? Dummy Variables: Gender, Education Level (High school, undergraduate, graduate) Independent Variable: Income level, credit score, number of credit cards, Age, Employment level Dependent Variable: Amount of credit card debt Required Minimums for Your Regression * At least 1 dummy variable – binary choice variable, like gender, race, region, political party. Something that isn’t a number. It is or it isn’t. We will talk about these. * You need n ≥ 150 observations. The more the better. But not less than 150 observations. * At least 5 total independent variables (x’s) Data * You will need a dataset containing your dependent variable (Y), your independent variable of interest (X) and other variables For those things you must hold constant, control variables (X2, X3, X4, etc). * Build your dataset in Excel and bring into Stata after I have approved it and when completely ready to run regressions. * Don’t expect to find your dataset all put together and ready for you to work with (not impossible, just really unlikely). Variables come from multiple sources. Keep up with where they come from so you can cite the source in your project. * If you are using panel data (for example, state level data over multiple years, or football player level data over multiple seasons) keep the entity and time variables intact in your dataset. You will need them. I will be showing an example of panel data in class. * One last tip – state and country level data sets are the easiest to find.

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Dependant Variable:

1. Average credit card debt per state: https://www.lendingtree.com/credit-cards/credit-card-debt-statistics/

https://lendedu.com/blog/average-credit-card-debt-us

Independent Variables:

1. Average income: https://www.marketwatch.com/guides/business/average-salary-by-state/

https://www.bls.gov/oes/current/oessrcst.htm

https://www.bls.gov/oes/current/oes_nat.htm

1. Average FICO score:

https://www.self.inc/info/average-credit-score-by-state-city/#table

1. Number of credit cards

: https://wallethub.com/edu/credit-card-statistics-by-state/134516

https://www.experian.com/blogs/ask-experian/average-number-of-credit-cards-a-person-has/

1. US states by median age: https://www.visualcapitalist.com/mapping-the-u-s-median-age-by-state/

https://public.opendatasoft.com/explore/dataset/us-cities-demographics/table/?flg=en-us

1. Education level, High School or Higher:

Generate Data

Education Level: 1 = High School, 2 = Some College, 3 = Bachelor’s Degree, 4 = Master’s Degree, 5 = Doctoral Degree

Dummy Variables:

1. Employment:

Employment: 0 = Unemployed, 1 = Employed

1. Gender:

Gender: 0 = Male, 1 = Female

https://www.federalreserve.gov/releases/chargeoff/delallsa.htm

https://www.federalreserve.gov/datadownload/Choose.aspx?rel=g19

https://www.federalreserve.gov/releases/g19/HIST/cc_hist_r_levels.html

https://www.kaggle.com/datasets/conorsully1/credit-score

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