NetCourse: Univariate Time Series with Stata
Learn about univariate time-series analysis with an emphasis on the practical aspects most needed by practitioners and applied researchers. Written for a broad array of users, including economists, forecasters, financial analysts, managers, and anyone who wants to analyze time-series data. Become expert in handling date and date-time data; time-series operators; time-series graphics, basic forecasting methods; ARIMA, ARMAX, and seasonal models.
We provide lesson material, detailed answers to the questions posted at the end of each lesson, and access to a discussion board on which you can post questions for other students and the course leader to answer.
Price: $295 Enroll now!
Stata Course content
Lesson 1: 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
Lesson 2: Descriptive analysis of time series
- The nature of time series
- Stationarity
- 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
Lesson 3: 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
Note: There is a one-week break between the posting of Lessons 3 and 4; however, course leaders are available for discussion.
Lesson 4: 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
- Unit-root processes
- ARCH
- A simple ARCH model
- Testing for ARCH
- GARCH models
- Extensions
Note: The previous four lessons constitute the core material of the course. The following lesson is optional and introduces Stata’s multivariate time-series capabilities.
Bonus lesson: Overview of multivariate time-series analysis using Stata
- 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
The above lists are not exhaustive. They are meant to give an idea of the level and scope of each topic.
Prerequisites:
- Stata 15 installed and working
- Familiarity with basic cross-sectional summary statistics and linear regression
Arrangement oversigt
Start dato | 18. Jan 2019, 18:00 |
Slut dato | 08. Mar 2019, 22:00 |
Pris | 295 US$ |
Det foregår | Online |