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

  • 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

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)

New Zealand

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