STATA TUTORIAL: LAB 1

1. STATA windows  The command window  The viewer/results window  The review of commands window  The variable window

2. Working with STATA A. Opening Data B. Using a “log” file C. Useful Commands D. Using a “do” file

A. Opening Data  Shows you your data  Check this frequently, especially after commands you are unsure about

A. Opening your data  If your data is in STATA format, then:  Go to “File”>”Open”>Browse Location where data stored>double click  In Command window type: use “Fill In Correct Path Name\filename.dta”  Practice with “Wage1.dta”

A. Opening your data-Data editor/browser  Data editor/data browser shows you your data  Go to “Window”>”Data Editor”  Click on “Data Editor” or “Data Browser” icons (editor: can modify data by typing in cell...like Excel; browser: locked, so can’t make changes)  Good to look at data when load data or after commands so that can understand structure of data.

A. Opening your data-Variable Window  Now that you have data loaded, you can see the variables that are included in the data listed in the variable window.  Name...name of variable  Label...description of what variable is  Type/Format...how STATA stores the variable format  Click on variable and it appears in command window.

A. Opening your data-What do the variables look like?  wage educ exper tenure nonwhite female married numdep smsa northcen south west construc ndurman trcommpu trade services profserv profocc clerocc servocc lwage expersq tenursq  What values do they take?  Wage...tenure, numdep are actual #’s  Nonwhite...servocc take values of 0 or 1...qualitative measures of some personal characteristics  Lwage...tenursq are transformations of other variables (ln, square)

A. Opening your data (advanced)  If your data is a comma delimited file: insheet using “filename.txt”  If your data is a raw data file:  It must have a dictionary file and you must use the “infile” command  infile using “dictionaryname.dct”  dictionary file will refer to data that has a “.dat” or “.raw” extenstion

B. The “log” file  The log file is an “output file”  Creates and saves a log with all the actions performed by STATA and all the results  How to open/close?  Go to “File”>“log”>“begin”  Go to “File”>”log”>”close”  How to view it later?  Go to“File”>“log”>“view”, and search for your filename, keeping in mind it has extension “.log”

C. Useful Commands  “describe”:  STATA will list all the variables, their labels, types, and tell you the # of observations  Two types of variables: 1. Numerical 2. String (usually appear in red in the data browser) You can convert a string variable to numerical using the “destring” command: ie. “destring var1, replace” or “destring var1, force replace”

C. Useful Commands  “summarize, sum, summ”  tells STATA to compute summary statistics (mean, standard deviations, and so forth) for all variables  useful to identify outliers and get an idea of your data  i.e. summarize (will do all variables)  i.e. summarize wage educ (just does wage and educ..note, no “,” between variables)

C. Useful Commands  How many observations are there?  What is the average value of wage?  What is the min and max of tenure?

C. Useful Commands  “tabulate, tab”  Shows the frequency and percent of each value of the variable in the dataset  i.e. tabulate tenure  i.e. tab wage (long list, to display all press space bar)  i.e. tab educ female (gives education by gender)

C. Useful Commands  “generate, gen”  Creates a new variable  gen weeklywage=wage*40  tab weeklywage  gen prevexper=exper-tenure  gen lwage=ln(wage)...gen newlwage=ln(wage)  gen expersp=exper*exper or gen expersq=(exper)^2

C. Useful Commands  “if” command allows you to use only a portion of the observations  tab wage if female==1  sum exper if educ>=13  gen expermomwkid=exper-1 if female==1  gen expermomwkid=exper-1 if female==1 & numdep!=0

C. Useful Commands  “reg” reg dependent variable independent variable (s)  reg wage educ Increase in education by 1 unit (year) is predicted to increase hourly wage by $0.54 R sq= When educ=0, wage is predicted to be -$0.90.

C. SLR Wage regression Increase in education by 1 unit (year) is predicted to increase hourly wage by $ increase by 6 years=6*$0.54=$3.24 R sq=0.1648; variation in education explains 16.4% of variation in wages When educ=0, wage is predicted to be -$0.90. Variance of estimator is

C. Reading the output table  SSTotal --The total variability around the mean.  SSResidual --The sum of squared errors:  SSModel (aka SSE)  Observe SSModel=SSTotal - SSResidual.  Note that SSModel / SSTotal is equal to , the value of R-Square

C. Reading the output table  Coefficients:  wagePredicted = *educ  Statistics (Ch. 4)  t and P>|t| - These columns provide the t-value and 2-tailed p-value used in testing the null hypothesis that the coefficient (parameter) is 0.  [95% Conf. Interval] - This shows a 95% confidence interval for the coefficient. (the coefficient will not be statistically significant if the confidence interval includes 0)

C. Reading the output table  After the regression, type:  predict wagehat, xb Tells us predicted value of wage, given that observations value of education  predict uhat, resid tells us portion of wage that is not explained by the independent variable(s)

C. Useful Commands  “replace”: replace value with a new one  replace wage=4 if wage<4  “drop”: drop entire variable or just some observations  drop prevexper  drop if educ<=8  “keep”  keep wage educ  Keep if educ>=8 Be careful with these commands!!

C. Operators  < less than  > greater than  <= less than or equal to  >= greater than or equal to  == equal to  !=. or ~= not equal to  & and  | or

E. The “do” file  A text file that you can type all your commands in and store.  Helpful to keep a file of what commands you run in case you want to re-run them later.  How to open/save a do file?  Go to “Window”>”Do-File Editor”  Or click on “New Do-File Editor”  Save the do file (.do)  To open saved do file, open a new do-file and search for where you saved it.

E. The “do” file:  Comments in your do file: /* */  STATA ignores the text that comes after * (does not execute them)  these lines can be used to describe what the commands are doing, or allows you to write comments. /*the following command summarizes the variable wage*/ sum wage

E. The “do” file  From the STATA do-file editor  click “do” for STATA to execute all commands  can highlight and click “do” to execute only the highlighted command lines  click “run” for STATA to execute all commands, but you won’t see results in viewer/results window  All the commands in a do-file can be typed into the command window and run from there, but this is helpful if you want to do same thing over and over.

E. The “do” file  Each command must have it’s own line  Stata will not run: sum wage sum educ But will run: sum wage sum educ sum wage educ

F. Save your data  Saving in Stata format:  save “Type in correct path name\file name.dta”  Go to “File”>”Save” or “Save As”

G. Other Commands  Increasing memory, variables  “set memory 200m”  “set maxvar 400”  Clear the file  “clear”  For long commands  # delimit ;  tells STATA that each STATA command ends with a semicolon...instead of line break  Do not forget the “;” and write this even after the comment lines that start with *.

G. Other Commands  sort  i.e. sort educ  i.e. sort educ female  by educ: summarize wage (Note, must sort first by educ before can use by educ)  Graphs  twoway (scatter wage educ )  histogram wage

H. MLR Wage Regression Including other covariates doesn’t change estimate on wage by much. R sq increases Variables have expected sign: Higher wage if have more experience, are married or have family(because probably very devoted worker), and live in metropolitan area. Women generally get paid less than men.