MSc Economic Policy Studies Methods Seminar. Stata Code and Questions sheet: Computer lab session 24 th October

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1 MSc Economic Policy Studies Methods Seminar Stata Code and Questions sheet: Computer lab session 24 th October Example 1. Modelling rents (cross-section) Section I A1. We can input the data into Stata in several ways. We ll add using a url when there is a dataset on the web we want. use A2. Obtain summary statistics (mean, min, max, standard deviation) of rent, house values and percent living in urban areas. Check there are no missing data in the dataset. sum rent hsngval pcturban A3. Plot histograms of rent, house values and per cent living in urban areas to get an idea of their distributions. Do these separately. (We could plot bar charts but there are 50 observations so might be a bit cluttered.) Use a boxplot to summarise. hist rent hist hsngval hist pcturban graph matrix rent hsngval hsnggrow A4. Plot scatter diagrams of rent against house values and percent living in urban areas to get an idea of possible correlation (use a separate line for each). twoway (scatter rent hsngval) twoway (scatter rent pcturban ) 1

2 A5. Add a best fitting line to the scatter plot of rent and housing value. twoway scatter hsngval rent lfit hsngval rent A6. Use the correlation coefficients between the three variables to formally check the correlation. Check and comment on the 1 s in the matrix table. cor rent hsngval pcturban Section II A7. Run an Ordinary Least Squares (OLS) regression of rent on house values and percent living in urban areas. regress rent hsngval pcturban A8. Always good to check residuals so do this and let s call these res1. predict res1, residuals A9. Obtain predicted values. predict pv, xb A10. Plot a histogram of residuals. What do these tell us? Go into the data editor and see where the largest residuals are. hist res1 2

3 A11. Plot a scatter chart of residuals against predicted values. Comment. twoway (scatter res1 pv) A12. Test for heteroskedasticity (null is that residuals have constant variance). estat hettest Section III (Building in first set of hypothetical comments) A13. Turn everything into logs using Stata s generate command. gen lnrent=log(rent) gen lnhval=log(hsngval) gen lnurpct =log(pcturban) A14. Estimate the equation using OLS again and comment on the new coefficients you have estimated. Also use the robust standard errors option as there is evidence of heteroscedasticity. What are these coefficients in words? Comment on the R 2. regress lnrent lnhval lnurpct, vce(robust) A15. Test whether the elasticities of rent with regard to housing value and urban percent are the same. H o : β 1 = β 2. What do you conclude? testparm lnhval lnurpct, equal 3

4 A16. Generate the log of population density and now add this into the previous equation as another independent variable / regressor. gen lnpopd =log( popd ) regress lnrent lnhval lnurpct lnpopd, vce(robust) A17. Test if this coefficient on lnpopd variable is significant and should stay in the equation. Section IV More advanced. (Addressing the second set of comments.) A18.You are worried that house value is endogenous. Why? (Look back on lecture 1 notes). Maybe an omitted variable or feedback effects? Save the residuals from the above model. predict res2, residual A19.Run a scatter plot of these residual against the hosing variable: lnhval. What should the residuals look like and how do they look? Are you worried? (There are formal tests to do this but you get the idea.) twoway (scatter res2 lnhval) A20. Family income has been suggested as an instrument for housing value. Check the correlation between this and housing value. Does it look encouraging as an instrument? gen lnfam=log(faminc) twoway (scatter lnfam lnhval) correl lnfam lnhval A21. Run an instrumental variable regression using family income as an instrument for housing value. Stata also allows you to set out the first stage regression: 4

5 ivregress 2sls lnrent lnurpct (lnhval = lnfam ), first A22. Interpret the coefficients under the IV and the OLS estimates. Are they different? A23. Check the first stage regression of how family income affects housing value. Was this a strong or weak instrument? Checking whether the F-stat is above 10 (a ball park figure that is suggested). (There are formal tests to do this but you get the idea.) A24. Was family income related to rent? Conclude? correl lnfam lnrent twoway (scatter lnfam lnrent ) 5

6 Example 2: Time Series (Irish GDP and Consumption) B1. Cut and paste from Example 2 tab in the excel spreadhseet. B2. You first need to tell Stata that both the log of GDP and Consumption are time series. generate time = q(1997q2) + _n -1 tsset time, quarterly B3. Plot the data over time and comment. Why might these series be called non-stationary? twoway (tsline lngdp lncon ) B4. Run an OLS regression of the log of personal consumption on the log of GDP. What is the R 2, coefficient and the t-statistic? Is this regression meaningful? regress lncon lngdp B5. Plot the first difference of the variables. Comment. Why might these series be called stationary? generate dlncon = d.lncon generate dlngdp = d.lngdp twoway (tsline dlncon dlngdp) B6. Calculate the difference between GDP and consumption and plot this and comment. (Looking at this difference is the precursor to cointegration- a technique for analysing such relationships. You might want to think about this if variables grow over time but are linked by a relationship). twoway (tsline diff) 6

7 Example 3: Panel Data (Airline costs) C1. This example we ll add in from a url. use clear C2. Run an OLS regression of cost on output, fuel and load. regress cost0 output0 fuel0 load C3. What do you notice about the way the data is stacked in the Data Editor? Because it s panel data we are potentially missing a lot by not taking this into account. Tell stata that it is panel data. xtset airline year C4. Run a fixed effects regression. This allows us to control for all factors specific to an ariline that we cannot observe. Give some examples? xtreg cost0 output0 fuel0 load, fe C5. Compare your results from OLS and fixed effects panel. 7

8 Example 4: Working with larger survey datasets (car data) D1. Enter the data that is built into Stata. The data looks many surveys you might encounter in terms of being a mix of continuous and discrete variables. sysuse auto D2. You often want basic summary information when working with a big data file, including number of observations in the file, the number of variables, the names of the variables, missing variables. describe D3. Another useful command for getting a quick overview of a large data file is the inspect command. inspect D4. You want to know something about the distribution of the repairs since The tabulate command is useful for obtaining frequency tables. tab rep78 D5. What is the repair history broken down by foreign and domestic cars? (Use a crosstab). tab rep78 foreign D6. What % of the foreign cars received a of 4 or 5 repair compared to domestic cars? You want to know what these are in percentages. 8

9 tab rep78 foreign, column D7. Often we want summary statistics broken down by groups or other discrete variables. What are the average mpg s for foreign and domestic vehicles? tab foreign, summarize(mpg) D8. Carry out a t-test of whether there is a statically significant difference in price between foreign and domestic prices? What do you conclude? ttest price, by(foreign) 9

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