
NC461
-
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
-
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
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
-