NC461

Univariate Time Series with Stata
Learn about univariate timeseries 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 timeseries data. Become expert in handling date and date–time data, timeseries operators, timeseries 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 timeseries analysis?

Timeseries data in Stata

The basics

Clocktime data


Timeseries operators

The lag operator

The difference operator

The seasonal difference operator

Combining timeseries operators

Working with timeseries operators

Parentheses in timeseries 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 timeseries analysis?

Timeseries data in Stata

The basics

Clocktime data


Timeseries operators

The lag operator

The difference operator

The seasonal difference operator

Combining timeseries operators

Working with timeseries operators

Parentheses in timeseries 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 goodnessoffit 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 timeseries analysis using Stata

VARs

The VAR(p) model

Lagorder selection

Diagnostics

Granger causality

Forecasting

Impulse–response functions

Orthogonalized IRFs

VARX models


VECMs

A basic VECM

Fitting a VECM in Stata

Impulse–response analysis

Lesson 4: Regression analysis of timeseries 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

Unitroot processes


ARCH

A simple ARCH model

Testing for ARCH

GARCH models

Extensions

Course prerequisites

Stata 15 installed and working

Course content of NetCourse 101 or equivalent knowledge

Familiarity with basic crosssectional summary statistics and linear regression

Internet web browser, installed and working
(course is platform independent)