**Course length:** 6 weeks (4 lessons)

**Dates:** 3 April – 22 May 2020

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.

- Stata 16 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

Note: This course is platform independent. You can use Stata on Windows, Mac or Linux.

**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