NetCourse 461: Univariate Time Series with Stata

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

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

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

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
• Nonstationary series and OLS regression
  • Unit-root processes
• ARCH
  • A simple ARCH model
  • Testing for ARCH
  • GARCH models
  • Extensions

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