GETTING STARTED WITH STATA. Sébastien Fontenay ECON - IRES

Transcription

1 GETTING STARTED WITH STATA Sébastien Fontenay ECON - IRES

2 THE SOFTWARE Software developed in 1985 by StataCorp Functionalities Data management Statistical analysis Graphics Using Stata at UCL Computer labs Socrate 30, 31-32, 33, 34, 54 and 68 Dupriez 143 Leclercq 74, 76, 77 and 78 Student licence to install on your personal computer valid during all your studies at the price of 20 euros

3 FINDING SUPPORT (1) Best documentation help command search keyword Stata website : Frequently Asked Questions Video tutorials Statalist Books Cahuzac, E., Bontemps, C. (2008). Stata par la pratique: Statistiques, graphiques et éléments de programmation. Cameron, A.C., Trivedi, P.K. (2009). Microeconometrics using Stata. Becketti, S. (2013). Time series using Stata. UCLA :

4 FINDING SUPPORT (2) For all your questions related to data management or analysis using Stata Website: By appointment only: Bâtiment Dupriez (office d010), 3 place Montesquieu

5 COURSE TOPICS Quick tour of Stata Working environment Writing commands Data management Inputting data Transforming data Data analysis Descriptive statistics Linear regression Exporting results

6 SECTION 1 QUICK TOUR OF STATA Working environment Writing commands

7 WORKING ENVIRONMENT The working environment is composed of 5 windows Results of commands Variables list and labels Review of commands Properties of variables and dataset Command window

8 WORKING ENVIRONMENT Three specific windows can be opened by clicking on the following icons Data editor/browser Display data in memory Viewer Display log and help files Do-file Editor Text editor to save/execute commands There are 3 main types of files used in Stata.dta data.do commands (do-file).smcl.log output (log file)

9 WORKING ENVIRONMENT Data Graphics Statistics All software functionalities are available from the dropdown menus Useful when you are unsure of commands to run or unfamiliar with available options Every command issued in this manner is echoed to the review and results windows e.g. sysuse auto.dta

10 WORKING ENVIRONMENT In order to use Stata effectively, you should always follow this three-step process: Open a do-file Choose your working directory cd "C:\Users\Me" mkdir stata_training cd stata_training - You can see the current working directory at the bottom left of the main window Start a log file (saving commands and their output) log using filename [, text append replace] - log close - log off on

11 SECTION 1 QUICK TOUR OF STATA Working environment Writing commands

12 WRITING COMMANDS Stata commands use a common syntax: [prefix :] command [varlist] [= exp] [if] [in] [, options] The square brackets denote qualifiers that are optional Italicized words are to be substituted by the user varlist denotes a list of variables exp is a mathematical expression Stata is case sensitive! (i.e. UPPERCASE!= lowercase)

13 WRITING COMMANDS Operators may be used to manipulate numerical or string variables Arithmetic + addition - subtraction * multiplication / division ^ raised to power Logical & and or! not ~ not Relational > greater than < less than >= > or equal <= < or equal == equal ~= not equal!= not equal Pay attention that a double equal sign (==) is used for equality testing

14 WRITING COMMANDS Logical and relational operators are particularly useful with if qualifiers to define the sample for analysis The if qualifier at the end of a command means the command is to use only the data specified command if exp list make if foreign==1 list if make=="volvo 260" list make price if price>=5000 & price<=7000 list make price if price<5000 price>7000 Note that character strings are enclosed in double quotes

15 WRITING COMMANDS You can refer to a list of numbers using the following shorthand 1/30 1 to 30 1/l 1 until last number f/-5 first to 5 th number before the end -5/l last five numbers Numlists are particularly useful with the in qualifiers to specify a range of observations to be used command in range list in f/10 list in -10/l list make price in 74

16 WRITING COMMANDS The by prefix repeats execution of a command on subsets of the data subsets are groups of observations that take the same value in a given variable (often a categorical variable) by varname: command - by foreign: list make If the dataset is not sorted, you should use the bysort prefix instead bysort varname: command

17 SECTION 2 DATA MANAGEMENT Inputting data Transforming data

18 INPUTTING DATA To open a dataset in Stata format (.dta): use use filename [, clear] sysuse - open example datasets installed with Stata To save a dataset in Stata format: save save filename [, replace] Stata can also import/export Excel files (.xls or.xlsx) import excel filename [, firstrow] export excel filename [, firstrow(variables)] By default, Stata opens/saves a dataset from/in the current working directory but you can specify another directory: use save "C:\Users\Me\Stata_training\dataset.dta" a web address: use

19 INPUTTING DATA Summary of the dataset describe: information on dataset in memory codebook: detailed description of variables Further explore data in memory count: number of observations list: display data in the results window Manipulate variables/observations keep wage educ exper drop in 1/10 sort wage

20 SECTION 2 DATA MANAGEMENT Inputting data Transforming data

21 TRANSFORMING DATA To create a new variable: generate generate newvar = exp [if] [in] exp may be a number, a character string or a mathematical function generate constant = 1 - Create a constant equal to 1 generate constant_text = "text" - Create a constant that contains the character string "text" generate logwage = ln(wage) - Create a variable equal to the natural logarithm of wage generate expersq = expr^2 - Create a variable equal to the square of exper

22 TRANSFORMING DATA To create specific variables using time series operators generate lag_gdp = L.gdp Create a variable corresponding to the first lag of gdp generate lead_gdp = F.gdp Create a variable corresponding to the first lead of gdp generate diff_gdp = D.gdp Create a variable corresponding to the first difference of gdp But before you should tell Stata that you are working with time series data using the command: tsset tsset time [, yearly monthly quarterly daily] Using system variables generate gdp_growth = ((gdp[_n] - gdp[_n-1]) / gdp[_n-1])*100 Create a variable equal to the growth rate of gdp

23 TRANSFORMING DATA To modify an existing variable: replace replace wage=20 if wage>=20 To rename an existing variable: rename rename wage hourly_wage You can also add a brief description to the variable using labels label variable educ "total years of education"

24 TRANSFORMING DATA When transforming data, one must be careful with missing values Missing values in Stata are coded with a. (period) Stata treats missing values as large numbers, higher than any other values of a given variable In certain cases you should use the if qualifier to exclude missing values generate rich = (wage>15) if wage<. or generate rich = (wage>15) if wage!=. or generate rich = (wage>15) if!missing(wage)

25 SECTION 3 DATA ANALYSIS Descriptive statistics Linear regression Exporting results

26 DESCRIPTIVE STATISTICS Categorical variables One-way table of frequencies tabulate female - The option [, missing] displays the total frequency of missing observations Two-way table of frequencies tabulate female married Continuous variables summarize gives the number of observations, the mean, the standard deviation, the minimum and maximum values summarize wage educ - The option [, detail] displays the main quantiles, the highest and lowest five values, the variance, as well as the skewness and kurtosis measures Pearson s correlation coefficient correlate varlist [, covariance]

27 DESCRIPTIVE STATISTICS Exploring data with graphs Distribution of a continuous variable: histogram histogram wage - the option [, normal] draws a normal density line on the plot Scatter plot between two variables: scatter scatter wage educ Evolution of time series: tsline tsline gdp - available only after tsset

28 SECTION 3 DATA ANALYSIS Descriptive statistics Linear regression Exporting results

29 LINEAR REGRESSION We seek to estimate the relationship between one dependent variable and a set of independent variables using the Ordinary Least Squares (OLS) estimator Classical linear model assumptions (Wooldridge, 2008): Model is linear in parameters Data are random sample of the population No perfect collinearity between independent variables Zero conditional mean of error term Homoskedasticity Normality of the residuals

30 LINEAR REGRESSION The model we want to estimate: log(wage) = β 0 + β 1 education + β 2 experience + β 3 tenure + u where: - wage is average hourly earnings in dollars - education is the number of years of education - experience is the number of years of labour market experience - tenure is the number of years with the current employer In Stata: regress logwage educ exper tenure

31 LINEAR REGRESSION Stata output

32 LINEAR REGRESSION Analysis of variance Sum of Squares (SS) Explained variance (model) Residual variance Total variance Degrees of freedom (df) Mean Squares (MS) SS divided by df

33 LINEAR REGRESSION Overall model fit Number of observations F-statistic p-value associated with the F-statistic testing the null hypothesis that all of the model coefficients are 0 R-squared proportion of variance in the dependent variable explained by the independent variables - SS(model) divided by SS(total) Adjusted R-squared Standard deviation of the error term MS(residual)

34 LINEAR REGRESSION Parameters estimates Dependent variable (1) Independent variables and intercept (2) Coefficients (3) Standard-errors (4) t-statistics (5) p-values associated with the t-statistics (6) testing the null hypothesis that a given coefficient is 0 95% confidence intervals (7) (1) (3) (4) (5) (6) (7) (2)

35 LINEAR REGRESSION Predicting fitted values and residuals predict wage_fitted e.g. 1, = 0, *0, *0, *0, predict wage_resid, r e.g. -0, = 1, , logwage educ exper tenure wage_fitted wage_resid 1 1, , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

36 LINEAR REGRESSION Incorporating categorical information into regression models Dummy variables (coded as 0/1) can be included as such in the regression regress wage educ exper tenure female Categorical variables with more than two categories must be included using the i. prefix regress wage educ exper tenure i.region Stata will automatically create dummy variables for each category and incorporate them in the regression except the reference category - You can use the prefix ib(x). instead to change the reference category

37 LINEAR REGRESSION Post-estimation tests Multicollinearity (Wooldridge, chapter 3, p99) estat vif - Rule of thumb, if variance inflation factor>10, multicollinearity problem Normality of the residuals sktest varname - testing the null hypothesis that variable follows a standard normal distribution swilk sfrancia varname - Shapiro-Wilk and Shapiro-Francia test Homoskedasticity (Wooldridge, chapter 8) estat hettest - Breusch-Pagan test, testing the null hypothesis of homoskedasticity estat imtest, white - White test, testing the null hypothesis of homoskedasticity The [, robust] option after regress gives heteroskedasticity-robust standard errors F-test: testing that a group of variables has no effect on the dependent variable joint hypotheses test (Wooldridge, chapter 4, p143) test var1 var2

38 SECTION 3 DATA ANALYSIS Descriptive statistics Linear regression Exporting results

39 EXPORTING RESULTS outreg2 allows to easily export the results of one or several regressions to Microsoft Office applications: Word, Excel to LaTeX outreg2 [estlist] using filename [, word excel tex] [estlist] refers to the list of estimation results previously saved using the command: estimates store estname

40 EXPORTING RESULTS regress logwage educ estimates store est1 regress logwage educ exper tenure estimates store est2 regress logwage educ exper tenure female estimates store est3 outreg2 [est1 est2 est3] using output, word