NetCourse^® 461: Introduction to Univariate Time Series with Stata

Course content

Lecture 1: Introduction

• 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

Lecture 2: Descriptive analysis of time series

• 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

Lecture 3: Forecasting II: ARIMA and ARMAX models

• 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

There is a week-long break between lectures 3 and 4 to allow more time for those who may fall behind and for more discussion from the participants.

Lecture 4: Regression analysis of time-series data

• 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

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

• Introduction
  • 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

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