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 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)

 

PROGRAM

 

LESSON I: 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

 

LESSON II: 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

 

LESSON III: 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

LESSON IV: 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

 

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

 

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

 

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