### CONTENT

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.

### PREREQUISITES

• Stata 18 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)

### PROGRAM

SESSION I: INTRODUCTION

1. Course outline
3. What is so special about time-series analysis?
4. Time-series data in Stata
• The basics
• Clocktime data
5. 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
6. Drawing graphs
7. Basic smoothing and forecasting techniques
8. Four components of a time series
9. Moving averages
10. Exponential smoothing
11. Holt–Winters forecasting

SESSION II: DESCRIPTIVE ANALYSIS OF TIME SERIES

1. The nature of time series
• Stationarity
2. Autoregressive and moving-average processes
• Moving-average processes
• Autoregressive processes
• Stationarity of AR processes
• Invertibility of MA processes
• Mixed autoregressive moving-average processes
3. The sample autocorrelation and partial autocorrelation functions
• A detour
• The sample autocorrelation function
• The sample partial autocorrelation function
4. A brief introduction to spectral analysis—The periodogram

SESSION III: FORECASTING II: ARIMA AND ARMAX MODELS

1. Basic ideas
• Forecasting
• Two goodness-of-fit criteria
• More on choosing the number of AR and MA terms
2. Seasonal ARIMA models
• Multiplicative seasonality
3. ARMAX models
4. Intervention analysis and outliers
5. Final remarks on ARIMA models

SESSION IV: REGRESSION ANALYSIS OF TIME-SERIES DATA

1. Basic regression analysis
2. Autocorrelation
• The Durbin–Watson test
• Other tests for autocorrelation
3. Estimation with autocorrelated errors
• The Newey–West covariance matrix estimator
• ARMAX estimation
• Cochrane–Orcutt and Prais–Winsten methods
4. Lagged dependent variables as regressors
5. Dummy variables and additive seasonal effects
6. Test for structural break
7. Nonstationary series and OLS regression
• Unit-root processes
8. ARCH
• A simple ARCH model
• Testing for ARCH
• GARCH models
• Extensions
9. Markov-switching models
• Markov-switching dynamic regression
• Markov-switching autoregression
10. Threshold regression
• A self-exciting threshold model
• A second threshold model
• Letting threshold choose the number of regimes

Note: The previous four session constitute the core material of the course. The following session is optional and introduces Stata’s multivariate time-series capabilities.

BONUS SESSION: OVERVIEW OF MULTIVARIATE TIME-SERIES ANALYSIS USING STATA

1. VARs
• The VAR(p) model
• Lag-order selection
• Diagnostics
• Granger causality
• Forecasting
• Impulse–response functions
• Orthogonalized IRFs
• VARX models
2. VECMs
• A basic VECM
• Fitting a VECM in StataImpulse–response analysis

Note: There is a one-week break between the posting of Sessions 3 and 4; however, course leaders are available for discussion.