API-208: Stata Review Session

Daniel Yew Mao LimHarvard University

Spring 2013

Roadmap

Importing Data

Data analysisData management

ProgrammingGetting Started

Getting Started: Orientation

COMMAND WINDOW: commands typed here

VARIABLES WINDOW: variable list shown here

RESULTS WINDOW: results and commands displayed here

REVIEW WINDOW: past commands appear here

Getting Started: Syntax

Getting Started: Syntax Example

Getting Started: Syntax Example

Getting Started: Useful Commands I

if

in

by

sum

help

ssc install

Getting Started: Useful Commands II

Arithmetic Operators

• “+” addition• “-” subtraction• “*” multiplication• “/” division• “^” power

Getting Started: Useful Commands III

Relational Operators

• “>” Greater than• “<” Less than• “>=” Equal or greater than• “<=” Equal or less than• “==” Equal to• “~=” Not equal to• “!=” Not equal to

Getting Started: Useful Commands IV

Logical (Boolean) Operators

• “&” = and– Example: A & B

• “|” = or– Example: A | B

A

A B

B

Getting Started: Example

Getting Started: Worked Example

Average share of ADB loans during first and second years on UNSCBetween 1985 and 2004

Average share of ADB loans during first and second years on UNSCBetween 1985 and 2004, for each country

Getting Started: Creating Do-files

Text file containing all commands relevant to analysis

Useful for batch processing

Getting Started: Creating Do-files

Getting Started: Commenting in Do-files

* Ignore stuff written on this line

/* Text Here*/ Ignore stuff written in between

Getting Started: Commenting in Do-files

Importing Data: Data Types

Stata Data .xls .csv

Data Management: Data Structure

Cross-sectional Time-series Panel

Data Management: Datasets

• merge: add variables across datasets.• append: add observations across datasets.• reshape: convert data from wide/long or

long/wide• rename: change the name of a variable.• drop: eliminate variables or observations.• keep: keep variables or observations.• sort: arrange into ascending order.

Data Management: Missing Data

Recode List-wise deletion

Multiple Imputation

Data Management: Outliers

Impossible values

Extreme values

Logarithmic function

Data Management: Modifying Data

generate: create new variable.replace: replace old values.recode: change values by conditions.label define: defines value labels (or

“dictionary”).label values: attaches value labels (or

“dictionary”) to a variable.

Data Analysis: Exploring Data

• summarize: descriptive statistics.• codebook: display contents of variables.• describe: display properties of variables.• count: counts cases.• list: show values.

Data Analysis: Analyzing Data

• tabstat: tables with statistics.• tabulate: one- or two-way frequency tables

(related: tab1 and tab2).• table: calculates and displays tables of

statistics.

Data Analysis: Worked Example

Exercise 1: Create an aidsize variable with three categories based on the amount of ADB loans received (adbconstant): small (0 to 99), medium (100 to 999), and large (1000 or more). Include labels.

Data Analysis: MLE

• regress: standard OLS.• Probit/logit: binary dependent variable.• oprobit: ordered probit regression.• ologit: ordered logistic regression.• xtreg: fixed, between, and random effects, and

population averaged linear models.• xtregar: fixed and random effects models with

AR(1) disturbance.

Data Analysis: Matching

• psmatch2: propensity score matching.• cem: coarsened exact matching.

Data Analysis: Interpreting Coefficients

Programming

Conclusion

Pattern recognition Self-learning Programming

Q&A

Thank you!