1 / 27

Introduction into STATA III: Graphs and Regressions

Introduction into STATA III: Graphs and Regressions. Prof. Dr. Herbert Brücker University of Bamberg Seminar “Migration and the Labour Market” June 27, 2013. 1 GRAPHS Present your data graphically

cairbre-troy
Download Presentation

Introduction into STATA III: Graphs and Regressions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction into STATA III: Graphs and Regressions Prof. Dr. Herbert Brücker University of Bamberg Seminar “Migration and the Labour Market” June 27, 2013

  2. 1 GRAPHS • Present your data graphically • It is usually helpful if you present the main information /vairables in your data set graphically • There are many graphical commands, use the Graphicsmenue • the simplest way is to show the development of your variable(s) over time • Syntax: • graph twoway line [variable1] [variable2] if … • graph twoway line wqjt year if ed==1 & ex == 1 • This produces a two-dimensional variable with the wage on the vertical and the year on the horizontal axis for education group 1 and experience group 1

  3. Making a graph

  4. Graph of mean wage in education 1 and experience 1 group

  5. Graph of migration rate in edu 1 and exp 1 group

  6. GRAPHS: Two Y-axes • Two axes: It might be useful to display two variables in different y-axes with different scales (e.g. wages and migration rates) • Syntax: • graph (twoway line [variable1] [variable2], yaxis(1)) (twoway line [variable3] [variable2], yaxis(2)) if … • graph (twoway line wqjt year, yaxis(1)) (twoway line mqjt year, yaxis(2)) if ed==1 & ex == 1 • This produces a two-dimensional graph with the wage on the first vertical axis (y1) and the migration rate on the second vertical axis (y2)

  7. GRAPHS: Scatter plots (I/II) • Scatter plots display the relations between two variables • Syntax: • graph twoway scatter [variable1] [variable2] if … • graph twoway scatter wqjtmqjt if ed==1 • This produces a two-dimensional scatter plot which shows the relation between the two variables

  8. GRAPHS: Scatter plots (II/II) • You can also add a linear fitted line: • Syntax: • graph twoway scatter [variable1] [variable2] if …|| lfit [variable1] [variable2] if … • graph twoway scatter wqjtmqjt if ed==1|| lfitwqjtmqjt if ed==1

  9. 2 Running regressions • The standard OLS regression command in STATA is • Syntax • regress depvar [list of indepvar ] [if], [options] • e.g. regress ln_wijtmijt $D_i $D_j $D_t

  10. The multivariate linear regression model The general econometric model: γi indicates the dependent (or: endogenous) variable x1i,ki exogenous variable, explaining the independent variable β0 constantorthe y-axisintercept (if x = 0) β1,2,k regressioncoefficientorparameterofregression εi residual, disturbanceterm

  11. Running a regression model Globals ! Regressioncommand Dependentvariable Independentvariables

  12. Running a Regression: Output

  13. How to interpret the output of a regression variance of model degreesoffreedom 1. Observations2. fit of the model 3. F-Test 4. R-squared5. adjusted R-squared 6. Root Mean Standard Error β1 95% confidenceinterval β0 analysis of significance levels

  14. Recall the Borjas (2003)-Modell • yijt = βmijt + si + xj + tt + (si ∙ xj) + (si ∙ tt) + (xj ∙ tt) + εijt • This model in STATA Syntax: • regress ln_wqjtmqjt $Di $Dj $Dt $Dij $Dit $Djt • where • ln_wqjt: dependent variable (log wage) • mqjt: migration share in educatipn-experience cell • $Di: global for education dummies • $Dj: global for experience dummies • $Dt: global for time dummies • $Dij: global for interaction education-experience dummies • $Dit: global for education-time interaction dummies • $Djt: global for experience-time interaction dummies

  15. What is a global? • A global defines a vector of variables • Defining a global: • STATA Syntax: • global [global name] [variable1] [variable2] …[variablex] • global Di Ded1 Ded2 Ded3 • Using a global e.g. in a regression: • regress [depvariable] [other variable] [$global name] • regress ln_wqjtmqjt $Di • This is equivalent to: • regress ln_wqjtmqjt Ded1 Ded2 Ded3 • Thus, globals are useful shortcuts for lists (vectors) of variables.

  16. An alternative to the Borjas (2003) model: • yijkt = βmijt + γk (zk∙ mijt) + si + xj + zk + tt + (si ∙ xj) + (si ∙ zk) + (xj ∙ zk) + (si ∙ tt) + (xj ∙ tt) + (zk ∙ tt) + εijt • where • zkis a dummy for foreigners (1 if foreigner, 0 if native) • γk is a coefficient, whichcapturesthe different impact on foreigners, • k (k= 0, 1) is a subscriptfornationality • Idea: theslopecoefficientγk issignificantly different fromzero, ifnatives andimmigrantsareimperfectsubstitutes in thelabourmarket. • Problem: Wehavetoreorganizethedataset such thatitdeliversthe wage andunemploymentrates etc. forforeignersand natives.

  17. 3 Panel Models • Very often you use panel models, i.e. models which have a group and time series dimension • There exist special estimators for this, e.g. fixed or random effects models • A fixed effects model is a model where you have a fixed (constant) effect for each individual/group. This is equivalent to a dummy variable for each group • A random effects model is a model where you have a random effect for each individual group, which is based on assumptions on the distribution of individual effects

  18. Panel Models • Preparing data for Panel Models: • For running panel models STATA needs to identify the group(individual) and time series dimension • Therefore you need an index for each group and an index for each time period • Then use the tsset command to organize you dataset as a panel data set • Syntax: • tsset index year • where index is the group/individual index and year the time index

  19. Preparation: Running the tsset command

  20. Running Regressions: Panel Models • Then you can use panel estimators, e.g. the xtreg estimator • Syntax • xtregressdepvar [list of indepvar ] [if], [options] • xtregressln_wijtm_ijt, fe • i.e. in the example we run a simple fixed effects panel regression model which is equivalent to include a dummy variable for each group (in this case education-experience group)

  21. Running a Panel Regression: command

  22. Running a Panel Regression: Output

  23. Running Regressions: Panel Models • There are other features of panel estimators which are helpful • Heteroscedasticity: • Heteroscedasticity: the variance is not constant, but varies across groups • xtpcse , p(h) corrects for heteroscedastic standard errors • xtgls , p(h) corrects coefficient and standard errors for panel heteroscedasticity, but may produce biased results depending on the group and time dimension of the panel • Note: p(h) after the comma is a so-called “option” in the STATA syntax

  24. Heteroscedasticity within a group Y x

  25. Heteroscedasticity in panel models across groups Y x

  26. Running Regressions: Panel Models • Contemporary correlation across cross-sections • Contemporary correlation: the error terms are contemporarily correlated across cross-sections, e.g. due to macroeconomic disturbances • xtgls , p(c) corrects for contemporary correlation and panel heteroscedasticity, but may produce biased results depending on the group and time dimension of the panel.

  27. Next Meeting • July 4! • Presentation: July 18 • Room RZ 01.02

More Related