report | 代做assignment | econ代写 | 经济代写 – Stata Assignment 2 Econ 120B Spring 2022Department of Economics, UCSDXinwei Ma

2023-12-10

Stata Assignment 2 Econ 120B Spring 2022Department of Economics, UCSDXinwei Ma

stata代写 | report | 代做assignment | econ代写 | 经济代写 – 这是一个经济的practice, 考察经济方面的理解, 包括了report等方面, 这是值得参考的assignment代写的题目

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  • You shouldsubmit your answers on Canvas, including your do-file.
  • The deadline of submitting this Stata assignment isJune 3, 11:59pm (PT). No late submission will be accepted.
  • This Stata Assignment will be graded on three scales: 0%, 5%, and 10%. (For example, if you are able to answer more than half of the questions correctly, your will get the full credit for this assignment.) – If your do-file does not run, however, we will subtract 2.5%.
  • Start your do-file with the following (include your name and PID in your do-file) /******************************************************************************* ECON 120B, Spring 2022 Stata Assignment 2 Name: PID: *******************************************************************************/ clear all // clear the environment/memory set more off sysuse nlsw88 // load the built-in dataset nlsw Please make sure your do-file is clearly documented to help us understand your code.
  • nlsw88is a built-in dataset that comes with Stata. It is an extract from the 1988 round of the National Longitudinal Survey of Mature and Young Women. Following is a summary of the variables in this dataset. idcode survey id age age race race, can take three values,white,blackorother married = 1 if is currently married, = 0 otherwise nevermarried = 1 if never married, = 0 otherwise grade current grade completed collgrad = 1 if graduated from college, = 0 otherwise south = 1 if lives in southern states, = 0 otherwise smsa = 1 if lives in standard metropolitan statistical area, = 0 otherwise ccity = 1 if lives in central city, = 0 otherwise industry industry, usetab industryto see the categories occupation occupation, usetab occupationto see the categories union = 1 if is in a union, = 0 otherwise wage hourly wage, measured in $ hours hours worked per week ttlexp total work experience, measured in years tenure current job tenure, measured in years More information on the original data can be found here: https://www.bls.gov/nls/orginal-cohorts/mature-and-young-women.htm
  1. In this exercise you will employ regression analysis to study how education, experience, and job tenure affect wage. First, consider the following regression model: lnwagei= 0 + 1 gradei+ 2 ttlexpi+ 3 tenurei+ui, To estimate this model, generate a new variable calledlnwagewhich is the natural logarithm ofwagetimes 100. (This will make it easier to interpret the coefficients. For example, a one unit increase ingradewill correspond to a 1 % increase in wage.) (a) Use theregresscommand to estimate the above model. What is the percentage change in wage when education increases by one year? How about job tenure? (b) To test the null hypothesis that 2 = 3, what is the t-statistic? What is the p-value? Will you reject the null hypothesis at the 10% significance level? Next, to study if education has a quadratic (nonlinear) effect onlnwage, consider the following regression model: lnwagei= 0 + 1 gradei+ 2 ttlexpi+ 3 tenurei+ 4 grade^2 i+ui, To estimate this model, generate a new variable which equalsgrade^2. Use theregresscommand to estimate the coefficients. (c) What is the value of 1? What is the 95% confidence interval? (Use the standard normal critical value 1.960 to find the confidence interval.) (d) What is the value of 4? Is it statistically significant at the 10% level? (e) For someone with 12 years of education, what is the percentage change in wage if she receives an additional year of education? (f) To test the null hypothesis that 1 = 4 = 0, what is the Bonferroni statistic? How many restrictions are in this hypothesis? What is the p-value? Will you reject the null hypothesis at the 5% level?
  2. In this exercise, you will conduct regression analysis with binary and categorical variables. (a) Use the commandtabulateto show the categories of the variableoccupationand their frequencies. What is the relative frequency of the categorySales? Please report a number between 0 and 1. (b) Use the same command, this time specifying the optionnolabel, to visualize the numeric values corresponding to the different categories ofoccupation. Which numeric value corresponds to the labelSales? (c) Use the commandsummarizewith the optionifto compute the sample mean ofwagefor workers withSales occupation. What is the average wage for workers withSalesoccupation? (d) Use the commandregress wage i.occupationto run a regression with binary variables for everyoccupation category. (Addingi. to a categorical variable will automatically generate a binary variable for each category.) Theoccupationwith numeric value 1 is used as the base group. Given the regression results, what is the average wage for workers withSalesoccupation? How does your answer compare to part 2(c)? (e) Which occupation has the highest average wage? How much is it? (f) Use a similar command as in step (d), this time to study the average hours for eachoccupation. Which occupation works the longest hours per week? How many hours on average for this occupation? Next, we follow a similar procedure as in steps (a)(d) to study the wage gap among different races. (g) Use the commandregress wage i.raceto run a regression with binary variables for everyracecategory. What is the average wage forwhite? (h) What is the wage gap betweenwhiteandblack(report a positive number)? What is the 95% confidence interval for this wage gap?
(i) Generate three binary variables for categories inraceto run a saturated regression instead of (g). What is the
average wage forwhite? How does your result compare to (g)?
  1. In this exercise, you will run multiple regressions with interaction terms. First, consider the following regression with an interaction between the two binary regressors,collgradandunion: wagei= 0 + 1 unioni+ 2 collgradi+ 3 unioncollgradi+ui, To estimate this model, generate an interaction term betweencollgradandunion. Use theregresscommand to estimate the regression coefficients. (a) What is the base category in this model? What is the average wage for workers in this base category? (b) What is the difference in average wage for non-college graduates in a union and non-college graduates not in a union (report a positive number)? (c) What is the difference in average wage for college graduates in a union and college graduates not in a union (report a positive number)? Next, consider the following regression with an interaction term betweenttlexpandunion: lnwagei= 0 + 1 unioni+ 2 ttlexpi+ 3 unionttlexpi+ui, To estimate this model, generate an interaction term betweenttlexpandunion. Use theregresscommand to estimate the OLS coefficients. (d) For non-unionized workers, what is the average percentage change in wage when experience increases by one year? (e) For unionized workers, what is the average percentage change in wage when experience increases by one year?