MEAFA workshop on Quantitative Analysis using Stata 12, 6 - 10 Feb 2012


9 Nov 2011: The workshop is now open for reservations. Places are limited and are reserved on a first-come first-served basis following the completion of the online Reservation Form.

Presenter of Days 4-5 on Advanced Time-Series methods

David Drukker is the Director of Econometrics at StataCorp LP. David is the chief developer of the time-series suite [TS] of Stata, including the recent additions on advanced time-series methods that were resealed with Stata 12. David is an active researcher and his most recent publications are on the application of dynamic panel data methods and the estimation of spatial-autoregressive models. David is probably the most appropriate person for teaching Advanced Time-Series Methods using Stata.

Presenter of Days 1-3 on Working Efficiently with Stata and Data Management, Programming and Graphing

Demetris Christodoulou is the Founding Director of MEAFA and now General Convenor. Demetris is the architect of the MEAFA Professional Development Workshops on Quantitative Analysis using Stata, that are widely recognised by industry, government and academia for their state-of-the-art content. Demetris's online material on the use of Stata are used by researchers from more than 75 countries, and are implemented by international research centres in their PhD research training programs.

Brief workshop description

You may attend any one day or any combination of the following days:

Day 1 (Monday, 6 February 2012): Working efficiently with Stata 12 and Data Management by Demetris Christodoulou, MEAFA General Convener

This day assumes no previous knowledge of Stata 12. It demonstrates ways to work efficiently with the software, how to customise/personalise the working environment, handling basic data structures, analysing different types of variables and various data management techniques. Examples of output management and tables will also be presented. This day is of interest to those who are new to Stata or have limited experience with Stata 12.

Day 2 (Tuesday, 7 February 2012): Introduction to Stata Programming by Demetris Christodoulou, MEAFA General Convener

This day assumes working knowledge of Stata but no knowledge of programming with Stata or any other software. By the end of this day you will be able to produce efficient, tractable, reproducible and automated routines for data management, statistical analysis, econometric estimation, creation of tables etc. This day is appropriate to those who wish to attain a deeper knowledge of Stata and achieve the aforemetioned attributes in their work. If you have no or limited experience with Stata 12 then you are strongly advised to attend Day 1 first.

Day 3 (Wednesday, 8 February 2012): Graphing with Stata 12 by Demetris Christodoulou, MEAFA General Convener

This day assumes working knowledge of Stata but no knowledge of graphing with Stata or any other software. The day provides an in depth analysis of Stata's graphing logic, syntax and capabilities. Graphing examples will be demonstrated for a variety of data structures. By the end of this day you should be able to produce informative, robust, complex and beautiful graphs using reproducible routines. If you have no or limited experience with Stata then you are strongly advised to attend Day 1 first. Programming elements from Day 2 will also be used for producing more complex graphs.

Days 4-5 (Thursday-Friday, 9-10 February 2012): Advanced Time-Series Methods by David Drukker, Director of Econometrics, StataCorp LP

These days assume basic knowledge of univariate time-series analysis and working knowledge of Stata. If you have no experience with Stata 12 then you are advised to attend Day 1 first. The two-day course begins with a quick overview of how to use Stata for basic time-series analysis and then it follows a steep learning curve for exploring the latest developments in time-series methods, including filters, unobserved components, structural VAR, state-space models, dynamic factor models and more.

Enrollment and Fees

You may attend any one day or any combination of days. See the descriptions above to determine which days are of most interest to you. Note that the 2-days on Advanced Time-Series Methods go together as a package (prices exclude GST):

  • Each one of Day 1, Day 2 and/or Day 3 at $550 per day
  • Days 4 & 5 on Advanced Time-Series Methods at $1250

Fees include extensive course material, do-files and data sets, use of computing facilities, temporary use of Stata 12 licenses and full catering.

Numbers are limited and places are reserved on a first-come first-served basis following the completion of the online Reservation Form. Successful attendees will be notified shortly after reservation and invoices will be issued accordingly. Due to the limited places, MEAFA maintains a no refund policy following payment. For more information on enrollment and fees contact

N.B. Proceedings from the workshop go to funding MEAFA PhD scholarships.


You may qualify for one of the following discounts:

  • 35% discount for a restricted number of non-employed full-time PhD students.
  • 10% discount for additional attendees from the same business organisation, governmental department or academic unit.

Venue and computing facilities

The workshop will take place at the computer labs of The University of Sydney Business School, at the ground level of Building H69, cnr Codrington & Rose streets (see interactive map).

PCs and Stata 12 licenses for Microsoft Windows will be provided onsite. It is also possible to to work on your own laptop but you will not be able to access the web. You can also install a temporary one-month license for Stata 12.


All days have the same schedule (catering provided throughout the day):

  • 08:40-09:00 - Welcome tea and coffee
    09:00-10:30 - Session 1
  • 10:30-10:45 - Morning break
    10:45-12:15 - Session 2
  • 12:15-13:15 - Lunch
    13:15-14:45 - Session 3
  • 14:45-15:00 - Afternoon break
    15:00-16:30 - Session 4
  • 16:30-17:00 - Buffer-time and user-specific questions

The computer labs will be accessible from 8am to 6pm every day.

Detailed Programme

Day 1 (Monday, 6 February 2012): Working efficiently with Stata 12 and Data Management

Session 1: Introduction to Stata 12 environment
The Stata environment; configuration; special features; updates; personalised system; obtain help and perform search; Stata syntax; working with do-files.
Session 2: Data handling and adding metadata
Data formats; Import, export, load and save datasets; simulated datasets; sorting and ordering; review and document the dataset; display formatting.
Session 3: Working with different types of variables
Categorical vs. continuous data; numerical, string and date/time variables; missing data; dummy variables; special purpose variables.
Session 4: Output management and special features
Logs for output; tables; export output; some statistical and estimation commands; prefixes; append and merge.

Day 2 (Tuesday, 7 February 2012): Introduction to Stata Programming

Session 1: Basics of Stata programming
Properly structured do-files; comments; writing long commands; do vs. run; quietly vs. noisily; combination of preserve and restore; the command display in programming; accessing Stata parameters and Stata constants; running self-contained do-files and viewing logs.
Session 2: It's all about Macros!
What is a Stata macro; local macros; global macros; numerical macros; string macros; counters; compound punctuation; macro evaluation; formatting macro output; nested macros.
Session 3: Special features of macros and loops
Incrementing/decrementing macros; combining incrementation with evaluation; macro expansion; preventing macro expansion; foreach loop; forvalues loop; nested loops; the -if- programming command.
Session 4: Automating routines and other special features
Capturing saved results; macro evaluation with saved results; scalars and precision; creating tables using stored results; the low-level command file; user-written commands for making tables; explicit subscripting.

Day 3 (Wednesday, 8 February 2012): Data Visualization in Stata

Session 1: Basics of graphing with Stata
Dialog boxes vs. do-file routines; inspecting the data prior graphing; reducing the data dimension to speed up graphing; setting range of variation; graph example - histogram; titles; axes; labels; bars; adding notes; the concepts of box, position, line, text, colour and font.
Session 2: Subgroups and overlays
Graphing by categorical groups; subgroup options; formatting graph text; using special characters; graph aspect and size; superimposing densities and other graphs; legends for multiple graphs; multiple axes; graph help files; saving and modifying graphs; graph export formats.
Session 3: Generalised syntax for overlaying multiple graphs
The -twoway- command; scattergraph; linear fits; nonlinear fits; polynomials; nonparametric density estimators; parametric density estimators; tiding the syntax; softcoded vs hardcoded syntax.
Session 4: Advanced graphing
Using loops for multiple overlays; combining multiple graphs side-by-side; recasting twoway plots; reproducing formatting; do-file options; existing graph schemes; creating your own graph scheme; the graph editor as a scheme maker; special graphs; relative values of graph parameters.

Day 4 (Thursday, 9 February 2012): Advanced Univariate Time-Series Methods

Session 1: A quick review of basic time-series analysis in Stata
Using Stata for time series analysis and the [TS] suite; managing and summarizing time-series data; moving average and autoregressive (ARMA) processes; stationary ARMA models for nonstationary data; multiplicative seasonal models; autoregressive conditionally heteroskedastic (ARCH) models; autoregressive fractionally integrated moving average models (ARFIMA).
Session 2: The Frequency domain and linear filters
Introduction to the frequency domain; deterministic versus stochastic trends; trend and cycle decompositions; band-pass and high-pass filters in Stata; Christiano-Fitzgerald random walk band pass filter; Baxter-King filter; Hodrick-Prescott filter; Butterworth square wave high pass filter.
Session 3: The unobserved components model
Introduction to the univariate unobserved components model (UCM); trend, seasonal, and cyclical components; a flexible approach to smoothing and decomposition; stochastic and deterministic trend components; static and dynamic forecasts of components; stochastic cycles.
Session 4: A residual bootstrap example
Application of advanced univariate time-series analysis with bootstrapped residuals.

Day 5 (Friday, 10 February 2012): Advanced Multivariate Time-Series Methods

Session 1: Vector autoregressive (VAR) models and structural VAR models
Introduction multivariate time-series regression and the vector autoregressive (VAR) model; estimating and interpreting impulse-response functions (IRFs), dynamic-multiplier functions, and forecast-error variance decompositions (FEVDs); structural VAR subject to short-run or long-run constraints; checking the eigenvalue stability condition; lag-order model selection diagnostics for VAR; pairwise Granger causality tests; forecasting after VAR.
Session 2: Cointegrating VAR models
Introduction to the vector error-correction model (VECM) with cointegrating variables; estimating the cointegrating rank in a VECM; LM test for residual autocorrelation; model diagnostics; stability condition of VECM estimates; lag-order selection statistics; forecasting after VECM.
Session 3: State-space models
Introduction to the linear state-space model; alternative forms of the Kalman filter; covariance-form syntax and error-form syntax; the local-level model; local-linear-trend model; the vector autoregressive moving-average model.
Session 4: Dynamic factor models and multivariate GARCH models
The dynamic-factor model; unobserved factors with VAR structure; the multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model; the diagonal vech parameterization; constant conditional correlations (CCC); dynamic conditional correlations (DCC); time-varying conditional correlations (VCC); multivariate normal and Students' t errors robust variance estimates; level and variance predictions; static and dynamic forecasts.

N.B. The precise content is subject to fine-tuning.

Reservation Form

Numbers are limited and places are reserved on a first-come first-served basis following the completion of the Reservation Form.