NetCourseNow^TM 461: Introduction to Univariate Time Series with Stata

Content: This course provides an introduction to univariate time-series analysis that emphasizes the practical aspects that are most needed by practitioners and applied researchers. The course is written to appeal to a broad array of users, including economists, forecasters, financial analysts, managers, and anyone who encounters time-series data.   The course includes access to the lecture material, detailed answers to the questions posted at the end of each lecture, and access to a discussion board on which students can post questions for the course leaders to answer. Prerequisites: Stata 12 or Stata 11, 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 Internet Explorer, Firefox, or Safari, installed and working (course is platform independent) Price: $595   Enroll in NCNow461 (group rates) How is a NetCourseNow different from a regular NetCourse?

What is a NetCourseNow?

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

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 NCNow461