NCNow461, Introduction to Univariate Time Series with Stata

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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