
3-day MEAFA Professional Development Workshop on Quantitative Analysis Using Stata, 23-25 July 2008
Download the Brochure (
1046Kb)
Workshop Description
The workshop is intended for social science researchers who wish to develop quantitative skills
for managing and analysing large-scale datasets, understand the theory of time series and
panel data research methods and their applications using real data, and learn Stata at the same time.
While the workshop assumes zero knowledge of Stata and basic understanding of statistics and econometrics,
it follows a steep learning curve covering almost the entire range of Stata’s data management capabilities
and most significant routines for time series analysis and panel data analysis. This is a hands-on workshop,
but theory will be thoroughly discussed before applying any routine.
Stata 10
Stata 10 is a complete, integrated statistical package for data management, statistical analysis, graphing and econometric estimation. Stata is fast, accurate and easy to use. For more information visit StataCorp's website.
Computing Facilities
The workshop will take place at the Faculty of Economics and Business computer labs at The University of Sydney. You do not need to bring your own laptop. Stata 10 licenses for Microsoft Windows will be provided.
Which day to attend?
The workshop spans over three days and you may attend any one, any two or all three days (fees vary on the number of days attended). Day 1 introduces Stata 10 and uses the software’s routines to cover a number of data management and data analysis techniques. Day 2 details the theory for modelling univariate time series, forecasting and bivariate causality relationships, and offers extensive applications using Stata. Day 3 details the theory for working with panel data structures and modelling static and dynamic features of panel data, and again offers extensive applications using Stata. If you have no or little experience with Stata then you should attend Day 1 before moving to the rest of the days.
Enrollment
Numbers are limited and places are reserved on a first-come first-served basis. To avoid disappointement and secure a place, complete the Reservation Form. Successful attendees will be notified shortly after reservation and invoices will be issued accordingly. MEAFA maintains a no refund policy following payment. For more information contact meafa@econ.usyd.edu.au
Fees
You may choose which days to attend and fees vary on the number of days:
$550 for attending any one day, $825 for attending any two days, and $1100 for attending all
three days (prices include GST). There is a 50% discount for a restricted number of places
for non full-time employed PhD and honours students.
Fees include extensive course material, data sets, lectures,
use of computing facilities, temporary Stata 10 licenses, full catering and opportunity
to network with fellow researchers.
Venue
Economics and Business Building H69, The University of Sydney, Ground Floor, Computer Labs, cnr Codrington & Rose Streets, NSW 2006 (see interactive map).
Presenters
- Dr Demetris Christodoulou, MEAFA Director, Discipline of Accounting (Day 1)
- Dr Maurice Peat, Discipline of Finance (Day 2)
- Dr Vasilis Sarafidis, Discipline of Econometrics and Business Statistics (Day 3)
- Mr Karl Keesman, Survey Design & Analysis Services Pty Ltd, Australian & New Zealand distributors for StataCorp (Day 1, and support for Days 2 and 3)
| Day 1: Wednesday 23 July 2008, Introduction to Stata and Data Management | |
| 08:40 | Welcoming tea and coffee |
| 09:00-10:40 | Introduction to Stata and Data Formats Stata environemnt; Configuration and features; updates; personalised system; directory management; obtain help and perform search; online sources; Stata syntax; data formats; import, export, load and save datasets; create pseudorandom datasets; review and document the dataset; ordering of dataset; display format |
| 10:30-10:45 | Morning break |
| 10:45-12:15 | Data Structures and Types of Variables Categorical data; continuous data; append and merge other datasets; reorganise datasets; numerical, string and date/time variables; manage missing data; create dummy variables; other special purpose variables; convert datasets into summaries |
| 12:15-13:15 | Lunch |
| 13:15-14:45 | Data Management Identify usable dataset; validate claims on data structure; identify duplicate observations; alarming and nonsensical data; benefits of filtering |
| 14:45-15:00 | Afternoon break |
| 15:00-16:30 | Output Management Logs for output; use of translators for exporting Stata files and output; copy and paste from Stata to text editors and spreadsheets; reproduction of past work; stored results; saved results; the command display; explicit subscripting; important prefixes |
| 16:30-17:00 | Questions and User-Specific Issues |
* The program's detail is subject to minor changes |
|
| Day 2: Thursday 24 July 2008, Time-Series Methods and Applications | |
| 08:40 | Welcoming tea and coffee |
| 09:00-10:30 | Introduction to Time Series Analysis Time series data structure; time-series frequencies; describing time series; graphing time series; timeline plots; range plot with lines and other useful time series graphs; applications using Stata |
| 10:30-10:45 | Morning break |
| 10:45-12:15 | Smoothing and Basic Stationary Processes Time-series smoothing; moving averages; exponential smoothing; nonlinear smoothing; stochastic difference equation models; stationarity; autoregressive AR series; moving average MA series; random walks and white noise; applications using Stata |
| 12:15-13:15 | Lunch |
| 13:15-14:45 | Complex Stationary Processes and Causality Relationship ARMA and ARIMA specifications; ARCH models; rationale of causality; Granger causality tests; Granger-Sims causality tests; seminal papers that have applied the causality idea; applications using Stata |
| 14:45-15:00 | Afternoon break |
| 15:00-16:30 | Model Selection and Forecasting Sample autocorrelation function; partial autocorrelation function; regression diagnostics; Box-Jenkins model selection; lag order and lag exclusion selection statistics; tests for white noise; Portmanteau test; Dickey–Fuller unit roots test; augmented Dickey–Fuller test; applications using Stata |
| 16:30-17:00 | Questions and User-Specific Issues |
* The program's detail is subject to minor changes |
|
| Day 3: Friday 25 July 2008, Panel Data Methods and Applications | |
| 08:40 | Welcoming tea and coffee |
| 09:00-10:30 | Introduction to Panel Data Analysis Advantages and disadvantages over cross-sectional, time-series and pooled methods of analysis; some panel data sets; balanced vs. unbalanced panels; panel data dimensions and frequencies; properties of estimators; unbiasedness; efficiency; consistency; applications using Stata |
| 10:30-10:45 | Morning break |
| 10:45-12:15 | Static Linear Models Formulation and estimation; one-way and two-way error components; fixed and random effects; the Least Squares Dummy Variable model; the Within, Between and GLS estimators; Hausman test; describing panel data; variance decomposition; graphing panel data; applications using Stata |
| 12:15-13:15 | Lunch |
| 13:15-14:45 | Dynamic Linear Models Nickell biases; Anderson-Hsiao IV estimation; the problem of weak instruments; the Generalised Method of Moments; testing for overidentifying restrictions; applications using Stata |
| 14:45-15:00 | Afternoon break |
| 15:00-16:30 | Issues Specific to Panel Data Analysis Testing and modelling for cross sectional dependence; sample selection, attrition and stratified sampling; applications using Stata; robust estimation, iid errors vs. AR(1) errors, cluster-robust errors; applications using Stata |
| 16:30-17:00 | Questions and User-Specific Issues |
* The program's detail is subject to minor changes |
|
