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:
- January 22–March 12, 2010 (details)
- Prerequisites:
- Stata 11, 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
- Introduction
- Course outline
- Follow along
- What is so special about time-series analysis?
- Time-series data in Stata
- The basics
- Clocktime data
- Time-series operators
- The lag operator
- The difference operator
- The seasonal difference operator
- Combining time-series operators
- Working with time-series operators
- Parentheses in time-series expressions
- Percentage changes
- Drawing graphs
- Basic smoothing and forecasting techniques
- Four components of a time series
- Moving averages
- Exponential smoothing
- Holt–Winters forecasting
Lecture 2: Descriptive analysis of time series
- Descriptive Analysis of Time Series
- The nature of time series
- Autoregressive and moving-average processes
- Moving-average processes
- Autoregressive processes
- Stationarity of AR processes
- Invertibility of MA processes
- Mixed autoregressive moving-average processes
- The sample autocorrelation and partial autocorrelation functions
- A detour
- The sample autocorrelation function
- The sample partial autocorrelation function
- A brief introduction to spectral analysis—the periodogram
Lecture 3: Forecasting II: ARIMA and ARMAX models
- Forecasting II: ARIMA and ARMAX models
- Basic ideas
- Forecasting
- Two goodness-of-fit criteria
- More on choosing the number of AR and MA terms
- Seasonal ARIMA models
- Additive seasonality
- Multiplicative seasonality
- ARMAX models
- Intervention analysis and outliers
- Final remarks on ARIMA models
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
- Regression analysis of time-series data
- Basic regression analysis
- Autocorrelation
- The Durbin–Watson test
- Other tests for autocorrelation
- Estimation with autocorrelated errors
- The Newey-West covariance matrix estimator
- ARMAX estimation
- Cochrane-Orcutt and Prais-Winsten methods
- Lagged dependent variables as regressors
- Dummy variables and additive seasonal effects
- Nonstationary series and OLS regression
- ARCH
- A simple ARCH model
- Testing for ARCH
- GARCH models
- Extensions
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
- Introduction
- VARs
- The VAR(p) model
- Lag order selection
- Diagnostics
- Granger causality
- Forecasting
- Impulse-response functions
- Orthogonalized IRFs
- VARX models
- VECMs
- A basic VECM
- Fitting a VECM in Stata
- Impulse-response analysis
Enroll in NC461
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