Home  /  Products  /  Training  /  NetCourses  /  NC461: Univariate time series with Stata

< Return to NetCourses

NetCourse® 461: Univariate time series with Stata



Discounts available for enrollments of
five or more participants.

Course length: 7 weeks (4 lessons plus overview of multivariate methods)
Dates: 19 July–6 September 2024
See course schedule.
See alternate dates.

Can’t wait? Enroll in NetCourseNow 461Learn more »

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 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)

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
  • Test for structural break
  • Nonstationary series and OLS regression
    • Unit-root processes
  • ARCH
    • A simple ARCH model
    • Testing for ARCH
    • GARCH models
    • Extensions
  • Markov-switching models
    • Markov-switching dynamic regression
    • Markov-switching autoregression
  • Threshold regression
    • A self-exciting threshold model
    • A second threshold model
    • Letting threshold choose the number of regimes

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