NetCourse 461: Univariate Time Series with Stata

Course length:

7 weeks (4 lessons plus overview of multivariate methods)

Price:

USD 295

Course leaders:

Miguel Dorta and Gustavo Sánchez

Prerequisites:

Stata 16 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.)

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