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NetCourse: 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 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.

Price: $295 Enroll now!

Stata Course content

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

  • 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 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

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

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

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

The above lists are not exhaustive. They are meant to give an idea of the level and scope of each topic.

Prerequisites:

  • Stata 15 installed and working
  • Familiarity with basic cross-sectional summary statistics and linear regression

Please click here to enroll in this online course!

Arrangement oversigt

Start dato 18. Jan 2019, 18:00
Slut dato 08. Mar 2019, 22:00
Pris 295 US$
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