NC461

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Univariate Time Series with Stata

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 analyse 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.

Next class:

Oct 3, 2019

to

Nov 21, 2019

Can't wait? Want work on your own schedule?  Register for the same course with NetCourseNow.

Lesson 1: 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 2: Descriptive analysis of time series

• 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 3: 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

Bonus lesson: Overview of multivariate time-series analysis using Stata

Lesson 4: 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

Course pre-requisites

• Stata 15 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)

• 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