Time Series Analysis and Forecasting with Stata

TBA
Cass Business School, 106 Bunhill Row, London, EC1Y 8TZ, UK.

Course Description
Course Programme
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Terms and Conditions
Stata Software

Timberlake Consultants Ltd, the UK distributor of Stata, invite you to attend a four days course covering the use of Time Series Analyses with Stata 11, the well known statistical software package software package developed by StataCorp (USA).

The Course - The course assumes little mathematical background on the part of the participants. It teaches theory, modeling, programming, and interpretation of the major time series models, along with interesting applications to business and risk analysis in finance on a Windows based platform. 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 course, given in English, is aimed at students, researchers, and forecasters interested in

  • Longitudinal analysis with Stata
  • Box-Jenkins Time Series Analysis with Stata
  • Seasonal Box-Jenkins Models
  • Outlier modeling
  • Dynamic Regression  Analysis with Stata
  • GARCH modeling with Stata
  • Forecasting with time series models
  • Forecasting evaluation
  • Policy and Impact Analysis with Stata
  • Financial Risk Analysis with Stata

Mathematical Background Required

  • High School Algebra
  • Basic Statistics

Helpful but not required background 

  • Linear or Matrix Algebra
  • Basic differential and integral calculus

Advantages - The course will

  • Provide an Introduction to Applied Time Series Analysis Theory, Modeling, and Forecasting with Stata
  • Review major Time Series Analysis and Forecasting Theory including Box-Jenkins ARIMA, Time Series Regression, and GARCH Modeling
  • Provide hands-on experience in time series analysis and forecasting models - each delegate is provided with a computer throughout the course

The Principal Lecturer – Dr Robert A. Yaffee.

Robert A. Yaffee, 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.

Cost - The cost of the course is:

1st Participant

£1730.00 + VAT = £1989.50

2nd Participant

£1540.00 + VAT = £1771.00

All 5-days:1st ptp

£2030.00 + VAT = £2334.50

All 5-days:2nd ptp

£1815.00 + VAT = £2087.35

The cost includes course materials, all lunch and 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.

Agenda

Day 1   Morning
8:30am coffee and Registration


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 lunch
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. Hands-On  Experience and Programming practice

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

Day 2  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

  • dentification
  • 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
 
Day 3  Session begins at 9:00am
1. Intervention (Impact) Analysis

  • Pulse interventions
  • Level Shifts
  • Testing for them

2. Outliers

  • Additive
  • Periodic Pulses
  • Innovational
  • Patches
  • Modeling outliers

3. Intervention modeling with Arimacheck

4. Hands-on programming

Lunch: 12-1:30pm
5. Dynamic Regression Models with Stata

  • Impulse Response functions
  • deterministic inputs
  • stochastic inputs
  • Dynamic Regression Analysis Linear Transfer Function methodology
  • Dynamic Regression modeling with arimacheck
  • Forecasting with Dynamic Regression Models
  • ut-of-sample
  • ex ante

Break 2:30-2:45pm
6. Cointegration

  • exogeneity
  • Granger causality
  • Tests for exogeneity
  • Error Correction models

4:00-5:00pm
Q and A  Hands on

Day 4:  Session begins at 9:00am
1. Autoregressive Error Models

  • First order correction theory: Cochran-Orcutt
  • Prais-winston models
  • Newey-west robust models
  • Regression diagnostics
  • autocorrelation tests
  • heteroskedasticity tests
  • parameter constancy tests

10:30-10:45 Break

2. Q and A

3. Hands-on programming

4. Robust time series analysis

  • semi-robust time series analysis
  • robust time series models
  • robust time series with arimacheck

12:00-1:30pm  Lunch                                   
5. GARCH models: Theory and programming

  • ARCH
  • Forecasting with ARCH
  • GARCH
  • Forecasting with GARCH
  • Forecast Evaluation with GARCH Forecasts
    • out-of-sample
    • ex ante
  • EGARCH
    • modeling leverage effects
  • Volatility smiles and skews
  • graphing
  • modeling
  • GJR Threshold GARCH
  • APGARCH


Break: 2:30-2:45

5. Recapitulation

  
6. Q and A

  
7.  Hands-on  programming


4:30PM – end

Terms and Conditions

Registration closes 5 calendar days prior to the start of the course.

Cancellations:

  • full fee returned for cancellations made over 28 calendar days prior to start of the course
  • half-fee returned for cancellations made 14 calendar days prior to he start of the course
  • no fee returned for cancellations made less than 14 calendar days prior to the start of the course.

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Last revised:04/09/2009