# NetCourse: Univariate Time Series with Stata

## July 16 : 08:00 - September 3 : 17:00 CEST

\$295

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.

## Course content

### 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

• 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
• Multiplicative seasonality
• ARMAX models
• Intervention analysis and outliers
• Final remarks on ARIMA models

### 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

### 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

## Prerequisites

• Stata 16 installed and working
• Basic knowledge of using Stata interactively (NetCourse 101: Introduction to Stata)
• Familiarity with basic cross-sectional summary statistics and linear regression

## Dates and Time

• July 16 – September 3, 2021
• 18:00 – 22:00 CET (6 – 10 PM CET) (5 – 9 PM UK/GMT)

## Details

Start:
July 16 : 08:00 CEST
End:
September 3 : 17:00 CEST
Cost:
\$295
Event Category:

Online

## Organizer

StataCorp LLC
View Organizer Website

Event Conditions

Course registration is binding. Upon cancellation more than 8 working days before the course start date, we invoice 50% of the course fee. Upon cancellation less than 8 working days before the course start date, we invoice the full course fee. Click here to read the full Terms and Conditions!