NetCourseTM 461: Introduction to Univariate Time Series with Stata
- Content:
- This course provides an introduction to univariate time-series
analysis that emphasizes the practical aspects that are most needed by
practitioners and applied researchers. The course is written to appeal
to a broad array of users, including economists, forecasters, financial
analysts, managers, and anyone who encounters time-series
data.
- The course includes access to the lecture material, detailed
answers to the questions posted at the end of each lecture, and
access to a discussion board on which students can post questions
for other students and the course leader to answer.
- Course leaders:
- Brian Poi, senior economist at StataCorp
- Gustavo Sanchez, senior statistician at StataCorp
- Course length:
- 7 weeks (4 lectures plus overview of multivariate methods)
- Dates:
- October 9–November 27, 2009 (details)
- Prerequisites:
- Stata 10, installed and working
- Course content of NetCourse 101 or equivalent knowledge
- Familiarity with basic cross-sectional summary statistics and linear regression
- Internet web browser, installed and working
(course is platform independent)
- Price:
- $295.00
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Course content
Lecture 1: Introduction
- Time-series data in Stata
- Working with dates
- Time-series operators
- Drawing graphs
- Simple smoothers and forecasting techniques
- Moving averages
- Exponential smoothers
- Holt–Winters forecasting
Lecture 2: Descriptive analysis of time series
- The nature of time series
- Autocorrelation
- White noise
- Stationarity
- Time-series processes
- Moving average (MA)
- Autoregressive (AR)
- Mixed autoregressive moving average (ARMA)
- The sample autocorrelation and partial autocorrelation functions
- Introduction to spectral analysis—the periodogram
Lecture 3: Forecasting II: ARIMA and ARMAX models
- Basic ARIMA models
- Using ARMA processes to model series
- Choosing the number of AR and MA terms
- Selecting the best model from information criteria
- Forecasting
- Seasonal ARIMA models
- Models with exogenous regressors—ARMAX models
- A brief tour of intervention analysis
- Additive outliers
- Level shifts
There is a week-long break between lectures 3 and 4 to allow more
time for those who may fall behind and for more discussion from the
participants.
Lecture 4: Regression analysis of time-series data
- Autocorrelation
- Testing for autocorrelation
- Obtaining Newey–West standard errors
- More on ARMAX models
- Seasonal effects
- Nonstationarity and unit-root tests
- Heteroskedasticity in time series
- Autoregressive conditional heteroskedasticity (ARCH) models
- Generalized ARCH (GARCH) models and extensions
- Testing for ARCH effects
The previous four lectures constitute the core material of the course.
The following lecture is optional and introduces Stata's
multivariate time-series capabilities.
Bonus lecture: Overview of multivariate time-series analysis using Stata
- Vector autoregressive (VAR) models
- Estimating VAR models
- Impulse–response analysis
- Forecasting
- Structural VARs
- Cointegration
- Testing for cointegration
- Vector error-correction (VEC) models
Enroll in NC461
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