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:

16 Jan 2020

to

5 Mar 2020

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

Lesson 1: Introduction

• Course outline

• 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

• 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

• 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