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NetCourseNowTM 461: Introduction to 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 lecture material, detailed answers to the questions posted at the end of each lecture, and access to a discussion board on which you can post questions for other students and the course leader to answer.
  • 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, such as Chrome, Firefox, Internet Explorer, or Safari, installed and working
  • (course is platform independent)

Enroll a group in NetCourseNow 461

How is a NetCourseNow different from a NetCourse?

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

Enroll in NetCourseNow 461

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