CONTENT

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 lesson material, detailed answers to the questions posted at the end of each lesson, and access to a discussion board on which you can post questions for other students and the course leader to answer.

 

PREREQUISITES

  • Stata 18 installed and working
  • Course content of NetCourse 101 or equivalent knowledge
  • Familiarity with basic cross-sectional summary statistics and linear regression
  • Internet web browser, installed and working (course is platform independent)

PROGRAM

 

SESSION I: INTRODUCTION

  1. Course outline
  2. Follow along
  3. What is so special about time-series analysis?
  4. Time-series data in Stata
    • The basics
    • Clocktime data
  5. 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
  6. Drawing graphs
  7. Basic smoothing and forecasting techniques
  8. Four components of a time series
  9. Moving averages
  10. Exponential smoothing
  11. Holt–Winters forecasting

 

SESSION II: DESCRIPTIVE ANALYSIS OF TIME SERIES

  1. The nature of time series
    • Stationarity
  2. Autoregressive and moving-average processes
    • Moving-average processes
    • Autoregressive processes
    • Stationarity of AR processes
    • Invertibility of MA processes
    • Mixed autoregressive moving-average processes
  3. The sample autocorrelation and partial autocorrelation functions
    • A detour
    • The sample autocorrelation function
    • The sample partial autocorrelation function
  4. A brief introduction to spectral analysis—The periodogram

 

SESSION III: FORECASTING II: ARIMA AND ARMAX MODELS

  1. Basic ideas
    • Forecasting
    • Two goodness-of-fit criteria
    • More on choosing the number of AR and MA terms
  2. Seasonal ARIMA models
    • Additive seasonality
    • Multiplicative seasonality
  3. ARMAX models
  4. Intervention analysis and outliers
  5. Final remarks on ARIMA models

 

 

© Copyright 1996–2024 StataCorp LLC

 

 

 

SESSION IV: REGRESSION ANALYSIS OF TIME-SERIES DATA

  1. Basic regression analysis
  2. Autocorrelation
    • The Durbin–Watson test
    • Other tests for autocorrelation
  3. Estimation with autocorrelated errors
    • The Newey–West covariance matrix estimator
    • ARMAX estimation
    • Cochrane–Orcutt and Prais–Winsten methods
  4. Lagged dependent variables as regressors
  5. Dummy variables and additive seasonal effects
  6. Test for structural break
  7. Nonstationary series and OLS regression
    • Unit-root processes
  8. ARCH
    • A simple ARCH model
    • Testing for ARCH
    • GARCH models
    • Extensions
  9. Markov-switching models
    • Markov-switching dynamic regression
    • Markov-switching autoregression
  10. Threshold regression
    • A self-exciting threshold model
    • A second threshold model
    • Letting threshold choose the number of regimes

 

 

Note: The previous four session constitute the core material of the course. The following session is optional and introduces Stata’s multivariate time-series capabilities.

 

 

BONUS SESSION: OVERVIEW OF MULTIVARIATE TIME-SERIES ANALYSIS USING STATA

  1. VARs
    • The VAR(p) model
    • Lag-order selection
    • Diagnostics
    • Granger causality
    • Forecasting
    • Impulse–response functions
    • Orthogonalized IRFs
    • VARX models
  2. VECMs
    • A basic VECM
    • Fitting a VECM in StataImpulse–response analysis

 

 

Note: There is a one-week break between the posting of Sessions 3 and 4; however, course leaders are available for discussion.