The Course- The course assumes little mathematical background on the part of the participants. The course shows how to apply these techniques to real-life social science, economic, business, financial, and medical data, with many examples on the reporting and interpreting of the results. Participants are welcome to bring their own data.

Who should attend the Introductory course- The course, given in English, is aimed at students, researchers, and forecasters interested in

• Basic Stata
• Basic cross-sectional statistics with Stata
• Longitudinal analysis with Stata
• Box-Jenkins Time Series Analysis with Stata
• Seasonal Box-Jenkins Models
• Forecasting with time series models
• Forecasting evaluation

Mathematical Background Required

• High School Algebra
• Basic Statistics

Helpful but not required background 

• Linear or Matrix Algebra
• Basic differential and integral calculus

The Principal Lecturer – Dr Robert A. Yaffee

Robert A. Yaffee, Ph.D., a research professor at New York University and a senior research scientist/statistician on a U.S. National Science Foundation grant, served as a senior research/statistical consultant at the Academic Computing Services of the New York University Information Technology Services from 1989 until spring 2004.  Dr. Yaffee is author of a forthcoming book entitled An Introduction to Forecasting Time Series using Stata (expected publication date winter 2009-2010), and an author of a recent textbook entitled An Introduction to Time Series Analysis and Forecasting with Applications of SAS and SPSS (Academic Press, 2000). Yaffee has written articles on the design and planning of statistical analysis, logistic regression analysis, along with a number of articles on the psychosocial aspects of pathological gambling.   He has lectured on the research methods in empirical research, theory and programming of structural equation models, event history analysis, complex sampling, categorical data analysis, time series analysis, and quantitative epidemiological analysis.
From 1995 through 2000, he held the position of research scientist/statistician at Downstate Medical Center , working under a National Institute of Mental Health grant to study depression and anxiety on the part of immigrant groups within Brooklyn .  Before joining New York University , he served as an associate research scientist at the Columbia University School of Public Health on a National Institute for Drug Abuse grant.  From 1986 through 1990, he served as a member of the editorial board of the Journal of Gambling Behavior and from 1990 to 2004; he has served on the editorial board of the Journal of Gambling Studies.

The course fees are:

First Day only

£310.00 + VAT = £356.50

1st Participant	£1170.00 + VAT = £1345.50
2nd Participant	£1050.00 + VAT = £1207.50

The cost includes course materials, lunch, refreshments and the use of computers. The number of delegates is restricted. Please register early to guarantee your place. If you need assistance in locating hotel accommodation in the area, request the help of our Training Department.

Morning 8:30am coffee and Registration

Day 1  
1. Introduction to Stata

•    Configuration of Stata (adding your own editor)
•    Free data sources
•    Variable construction ( including date and time variables, etc.)
•    Variable transformations (recoding, replacing, functional, and power)
•    Missing value management  (single and multiple imputation)
•    Codebooks
•    Dataset construction:  cross-sectional, longitudinal, time series, panel, survival
•    File management  (appending and merging, wide-long conversion) 

2. Item analysis and Scale construction

•    Reliability and validity analysis

3. Data cleaning

•    Range and consistency checks
•    file comparison

4. Exploratory graphical visualization

• Histograms , stem-and-leaf plots, bar graphs, dot-plots, line graphs, scatterplots,  pie charts,  panel   graphs,  reference lines, and annotation

5. Research Project planning

•    Power and sample size analysis
•    Sampling  (simple random, stratified, clustered, stratified -clustered)
•    Attrition  and censoring in longitudinal studies
•    Hypothesis testing

5. Summary statistics for sample description

6. Categorical data analysis

•    Tabulations
•    Cross-tabulations

 7. T-tests

•     One-sample
•     Two independent samples
•     Paired

8. ANOVA

•    Assumptions and tests for them
•    One-way ANOVA
•    Two-way ANOVA

9. Random, Fixed, and Mixed models

10. Repeated Measures WSANOVA

11. Regression analysis

•    OLS
•    Assumptions and tests for them
•    Modeling strategies and critiques
•    General-to-specific, Hierarchical, All possible subsets, and Bootstrapping

12. Robust regression

•     Heteroscedastically consistent estimation
•     Outlier down-weighting

Day 2 Morning
9:00    
1:  Basic Time Series Analysis Concepts

•      definition of a time series
•      cycles
•      trends
•      seasonality
•      lags, leads, differences
•      nomenclature
•      Expectation notation
•      Summation notation

10:30 Break  

2. Time Series Setup with Stata

• inputting time series data
• time-date functions and applications
• importing and exporting time series data
• graphing Time Series with Stata
• preliminary analysis of time series with Stata

12:00 noon-1:30pm unch
3. Stationarity

• covariance stationarity
• strict stationarity
• Dickey Fuller tests
•        theory
•        programming dfuller tests
• Augmented Dickey-Fuller tests
•         theory
•         programming
• Phillips-Perron tests

4. Autocorrelation

2:30-2:45pm Break
5. Moving averages

• Theory
• Types
• Characteristic ACF and PACF patterns
• Programming the ACF and PACF
• White noise Significance tests

6. 4:00  Hands-On  Experience and Programming practice 

• Stationarity diagnosis and transformations
• ARIMA identification
• Integrated processes    
• AR processes
• MA processes
• ARMA processes

Day 3  Session begins at 9:00am
1. ARIMA modeling

• estimation
• estimation algorithms
• full maximum likelihood
• conditional maximum likelihood
• diagnosis
• Intervention modeling
• model fitting

Break  10:30
2.  Seasonal ARIMA models

• Identification
• Estimation
• diagnosis
• model fitting

Lunch 12.00-1:30
Afternoon 1:30-2:30

3. Forecasting theory

• sample segmentation
• segment lengths
• in-sample v. post-sample forecasting
• point forecasts
• interval forecasts
• forecast profiles
• out-of-sample forecasts
• ex ante forecasts
• one-step ahead forecasts
• dynamic forecasts
• structural forecasts
• combining forecasts

2:30-2.45 PM Break
4. Forecasting Evaluation

• Tests of forecast bias
• Tests of forecast accuracy: out-of-sample and ex-ante
• MSFE
• MAE
• MAPE
• MdAPE
• Theil’s U
• Diebold-Mariano test of comparative forecast evaluation

5. Forecasting Graphics

6. 3:00- 5:30 Hands  on  ARIMA modeling and forecasting