Statistical Software
Date： 2018-10-24 Views: 18

Statistical Software

Course No.: SMJ2223122     Credit(s):2

  

Course Description

This course will equip students with a sufficient knowledge of Stata such that they are can handle and analyze different types of data. The emphasis of the course is on the practical issues relating to data analysis and modeling rather than econometric theory. The overriding objective of the course will be to ensure that the students are competent and confident in econometric analysis of data. The course encompasses a number of key fields in empirical analysis. The students will be shown how to analyze the data and how to estimate reliable econometric models using Stata. Throughout the course, the students will be shown how to avoid the numerous pitfalls that inexperienced researchers often fall into.

Course Learning Outcomes

The course aims to introduce participants to the basic usage of Stata for analyzing business and economics data. An overview of the main Stata functions will be given by showing them applied to real data examples. The course has two objectives: on one side the Stata structure and philosophy will be presented, and on the other side the course will demonstrate the potentialities of the software itself for analyzing data by making use of many different examples.

Upon successful completion of this course, students should be able to:

1.generate and manage Stata files

2.produce descriptive statistical reports using tables, summary measures and graphs

3.estimate linear regression models

4.understand and use at a basic level the Stata programming language 


Relationship to Other Courses

Pre-requisites: Statistics, Econometrics, Probability Theory

Textbook and Reading Lists

Textbook:

Lawrence C. Hamilton, Statistics With STATAVersion 12(8th edition).Tsinghua University Press, 2011.6.

Suggested reading lists:

Acock, A. C., A Gentle Introduction to Stata (2nd edition). Stata Press, 2008.

Kohler, U., Data Analysis Using Stata (2nd edition). Stata Press, 2008.

Mitchell, M. N., A Visual Guide to Stata Graphics(2nd edition).Stata  Press, 2008.

Cameron, A. C. e Trivedi, P. K., Microeconometrics Using Stata. Stata Press, 2009.

Baum, C. F., An Introduction to Stata Programming. Stata Press, 2009.

Baum, C. F., An Introduction to Modern Econometrics Using Stata. Stata Press, 2006.

Rabe-Hesketh, S. e Everitt, B. S., Handbook of Statistical Analyses Using Stata (4th edition). Chapman & Hall/CRC, 2006.

Recommended Software

Stata version 12.

Course Assessment

Item

Title

Weighting (%)

1

Task   in home

10%

2

Test   and Questions in class

20%

3

Final   Assignment

70%

Course Schedule

Week

Topics

Text

1-2

Introduction to STATA

·Working with   Stata: menu vs. command line vs. do files

·Help files,   online PDF documentation since Stata 12

·Creating   empty datasets and copy/pasting data

·Data import:   different ways of importing data

·Describing   the data

Chapters 1

3-4

Data   sources

·Import data   from main public data sources: World Bank (WDI), Penn Tables,

·Eurostat,   ECB, …

·Missing   values: “.” vs. “99”

·

Chapters 2

5 -6

Data   manipulation

·Generating   new variables. “Generate” vs. “Egen”.

·Dropping   variables

·Sorting

·Recode,   group

·Labelling   variables and values

·Logical   expressions

Chapters 3

7-9

Basis   statistical routines

·Mean,   standard deviation, correlation

·Percentiles

·(t-)Test on   mean difference. Compare groups within one variable, compare two

·variables.

·Cross-tabulation   of two binary variables and corresponding tests (Pearson)

·Cross-tabulation   of two discrete variables and corresponding tests (Pearson)

·OLS with one   explanatory variable

·Internal variables:   _coef, _se

·More stored   information: “Ereturn list”, “matrix list e(vce)”

·Postestimation   commands.

·

Chapters 4

10-11

Programming   in do files

·If condition   

·Loops

·Commenting

·

Chapters 5

12-13

Graphing   (here menu can be useful)

·Line plot.   Legend, labels, shapes, colors, …

·Scatter plot   

·Combining   graphs: “twoway”, e.g. scatter with regression line

·Histogram

·Kernel   density, intuitive discussion of bandwidth

·Step   function for cdf

Chapter 6

14-15

Panel   data

·Data   structure: Wide vs. long

·Reshape

·Xtset

·Xtdes

·

Chapter 7

16-17

Time   Series data

·Tsset

·Lag and   forward operator

·First   difference and dlog

Chapter 8

18

Presenting   results

  

Chapter 9