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ASSIGNMENT 3

Submit your answer to the following questions in a word document. Clear label each part and answer.

Include screenshots to clip R output as necessary.  If you are not sure how to take a screenshot then use google to look up the hotkeys for the snipping tool in windows or mac.

PART I:

In this assignment you will produce a small documents outlining a set of your own findings from the dataset “gpa1” from the R package “Wooldridge”.

You can install and load this package using the following code:

install.packages("wooldridge")
library(wooldridge)

You can find documentation for this data set by using the R command:

?gpa1

Your goal is to explore the determinants of college GPA.

1. Choose your variables and provide documentation

You may choose any 2 continuous variables and 2 binary variables to explore. (4 in total).

List the variables names and a short description.

2. Provide summary statistics

In a neatly formatted table provide summary statistics of the dependent variable and your 4 variables.

Report. Number of Observation, Mean, Std Deviation, Max and Min, 25^th and 75^th Percentile.

Hint: use the command sumtable() which relies on the packages “kableExtra”, “vtable”.

A useful example of this command resides in the R Script associated with the midterm solutions.

3. Run and report separate regression results for each of your 4 selected variables.

Using the lm() command run each of your regressions and store the results.

Ex. MyModel1 = lm(myY ~ myX1, myData)

Report coefficients, heteroskedastic robust standard errors, and values for each model in one table.

Hint: We have typically summarized our results using the summary() or sum() commands. Instead summarize multiple models at one time by using the export_summs() command from the “jtools" package.

Ex. export_summs(MyModel1, MyModel2, MyModel3, MyModel4, robust=T)

4. Interpret your results for a model with a continuous X variable.

For the continuous variable model with the most explanatory power:

- identify and  and whether they statically significant

- provide an interpretation of the coefficients

5. Interpret your results for a model with a binary X variable.

For the binary variable model with the most explanatory power:

- identify and  and whether they statically significant

- provide an interpretation of the coefficients

6. Provide a brief summary of your results.

a. Explain why you choose the variables you choose.

b. Briefly compare the results across each of your models.

c. Explain whether the results met your expectations.

PART II

Check out the paper:

Baylis, Patrick, Severin Borenstein, and Edward A. Rubin. When we change the clock, does the clock change us?. No. w30999. National Bureau of Economic Research, 2023.

Read the abstract for this paper.

Answer the following questions.

1. What is the model the authors estimate (Look in the DATA section of the paper).

2. Identify the key variables of interest and their associated parameters (coefficients).

3. Provide a description of the two key independent variables.

4. List the three key dependent variables.

5. Examine Table 1, Panel A, Column 1.

a. Identify the coefficients of interest. Is it statistically significant?
hint: Table 3 has a more complete table note. Read that table note to help you understand what is in parenthesis for table 1.

b. Provide an interpretation the coefficients of interest.
Note: You can also find an interpretation in DATA section below the model.

6. Are the results in Table 2, Panel A, Column 1 and Table 3, Panel A, Column 1 consistent with the results in examined in part e.

7. Why did the authors conduct this research? What conclusion do you reach on this issue?