The STATA program is designed for use in Econometric Analysis. Prior knowledge of Econometrics would be helpful.The program is apt for UG/PG Students, Research Scholars and Working Professionals with Economics Background.


Basic Knowledge of Statistics & Econometrics will be helpful

Faculty Profile

She is an Assistant Professor, Department of Economics, with a prominent college of University of Delhi. She has been teaching papers on statistics, mathematical economics, econometrics, macroeconomics and managerial economics. She has a doctorate from the Faculty of Management Studies, University of Delhi. She is well versed with econometric software’s like Eviews, STATA and SPSS. Her research interests include open economy macroeconomics, managerial economics, development economics and public policy.



  • Running Stata
  • Stata Interface
  • Introduction to Data, Dictionary, Do and Log files
  • Entering Data and Importing Data, Saving and Exporting Data
  • Combining and Appending Data


  • Basic Data Commands
  • String Variables
  • Labels
  • Dropping Variables
  • Operators
  • gen, egen and if command
  • Examining Data
  • Descriptive Statistics
  • Summarizing and Investigating
  • Correlation
  • Tab and Tabstat Commands
  • Crosstabs- building two way, three way and four way tables


  • Stata Graphics
  • Histograms
  • Scatterplots
  • Line Plots
  • Pie Charts
  • Bar Charts
  • Graph Matrix
  • Graphing with Do files
  • Editing Graphs


  • Linear Regression (Single Variable and Multiple Variables)
  • Post Regression Testing
  • Testing of OLS Assumptions
  • Presenting Regression Estimates-esttab
  • Working with Different Functional Forms


  • Generating Categorical Variables
  • Regression with Indicator/ Dummy Variables
  • Running Regressions with Subsets of Data
  • Use of Interaction Terms
  • Regression with Qualitative and Quantitative Variables


  • Instrumental Variables Regression in Stata
  • Two Stage Least Squares
  • Hausman Test


  • Basic Time Series Analysis,
  • Setting as Time Series: tsset
  • Filling gaps in Time Variables
  • Generating Lags and Leads
  • Running Regressions with Lagged Variables
  • Correlograms
  • Lag Selection
  • Checking for Stationarity
  • Testing for Breaks
  • VAR
  • Granger Causality using OLS & VAR
  • VECM


  • Binomial Logit and Probit Models
  • Multinomial Logit
  • Marginal Effects