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NetCourseNowTM 461: Introduction to Univariate Time Series with Stata



Discounts available for enrollments of
two or more participants.

NetCourseNow is a self-paced, personalized learning experience. All lectures will be posted at once, and you will be given the email address of your personal NetCourse instructor, to whom you can email questions about the lectures. Enroll now, and begin when you're ready.

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.


  • Stata 13 or Stata 12, installed and working
  • Course content of NetCourseNow 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

Lecture 1: Introduction

  • Time-series data in Stata
    • Working with dates
    • Time-series operators
  • Drawing graphs
  • Simple smoothers and forecasting techniques
    • Moving averages
    • Exponential smoothers
    • Holt–Winters forecasting

Lecture 2: Descriptive analysis of time series

  • The nature of time series
    • Autocorrelation
    • White noise
    • Stationarity
  • Time-series processes
    • Moving average (MA)
    • Autoregressive (AR)
    • Mixed autoregressive moving average (ARMA)
  • The sample autocorrelation and partial autocorrelation functions
  • Introduction to spectral analysis—the periodogram

Lecture 3: Forecasting II: ARIMA and ARMAX models

  • Basic ARIMA models
    • Using ARMA processes to model series
    • Choosing the number of AR and MA terms
    • Selecting the best model from information criteria
  • Forecasting
  • Seasonal ARIMA models
  • Models with exogenous regressors—ARMAX models
  • A brief tour of intervention analysis
    • Additive outliers
    • Level shifts


Lecture 4: Regression analysis of time-series data

  • Autocorrelation
    • Testing for autocorrelation
    • Obtaining Newey–West standard errors
    • More on ARMAX models
  • Seasonal effects
  • Nonstationarity and unit-root tests
  • Heteroskedasticity in time series
    • Autoregressive conditional heteroskedasticity (ARCH) models
    • Generalized ARCH (GARCH) models and extensions
    • Testing for ARCH effects

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

  • Vector autoregressive (VAR) models
    • Estimating VAR models
    • Impulse–response analysis
    • Forecasting
  • Structural VARs
  • Cointegration
    • Testing for cointegration
    • Vector error-correction (VEC) models
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