Location: NY, USA

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



Agenda

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

* Theory
* Types
* Characteristic ACF and PACF patterns
* Programming the correlograms
* Box-Ljung significance tests

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

http://www.timberlake.co.uk/training...tataTSUSA.html