Applied Time Series Using Stata

ARIMA, VAR, VECM, ARCH, GARCH, and structural breaks.

What you'll learn

Understand deterministic and stochastic trends

Identify stationary time series

Determine optimal ARIMA models

Capture policy changes using intervention models

Estimate vector autoregressions and their dynamics

Understand vector error correction models

Explore panel vector autoregressions

Become a confident user of Stata

Requirements

Basic training in applied data analysis would be useful. I recommend my Udemy course Getting started with Stata, which provides a detailed introduction to data analysis and Stata.

Description

This course covers univariate and multivariate time series models, including ARIMA, vector autoregressions, and vector error correction models. In addition, we explore cointegration and panel VARs, which are usually not covered in time series courses. The course starts with an introduction to time series, stationarity, and unit root testing. Then we establish the order of integration of time series before moving into autoregressive integrated moving average models (ARIMA). Intervention analysis is a useful extension of ARIMA models. This method can detect the anticipation of events such as policy changes. Multivariate models such as VARs and VECMs will be covered extensively in this course. Short-term dynamics and long-run equilibrium conditions between time series can be studied using impulse-response functions and cointegration. Most importantly, we will discuss structural break detection, which is crucial in enhancing our ability to forecast time series. Structural breaks can occur at known and unknown points in time. We will learn about methods that can find optimal breakpoints. Furthermore, we will construct ARCH and GARCH models to predict the conditional variance of time series. All material is available on Udemy. You can use older versions of Stata to conduct the analyses. Come join us. Let’s enjoy the Joy of Data Analysis!

Overview

Section 1: Unit 1 Introduction

Lecture 1 S1 Course Introduction

Lecture 2 S2 Introduction to Stata

Lecture 3 S3 Access to Stata

Lecture 4 S4 The Purpose of Time Series Analysis

Section 2: Unit 2 Time Series Analysis and Forecasting

Lecture 5 S5 Deterministic Models

Lecture 6 S6 Food Prices

Lecture 7 S7 Stationarity

Lecture 8 S8 Data Generating Process

Lecture 9 S9 Unit Root Test

Section 3: Unit 3 ARIMA

Lecture 10 S10 Introduction to ARIMA

Lecture 11 S11 Identifying an ARIMA model

Lecture 12 S12 Seasonality

Section 4: Unit 4 Intervention Analysis

Lecture 13 S14 Introduction to Intervention Analysis

Lecture 14 S15 Modeling lockdowns

Section 5: Unit 5 Vector Autoregression

Lecture 15 S16 Introduction to VARs

Lecture 16 S17 Understaning house prices

Lecture 17 S18 Estimating VAR models

Lecture 18 S19 Stability

Lecture 19 S20 Impulse Response Functions

Section 6: Unit 6 Cointegration and VECM

Lecture 20 S22 Data for pairs trading

Lecture 21 S23 The Main Ideas

Lecture 22 S24 Implementation in Stata

Section 7: Unit 7 Modelling Conditional Volatility

Lecture 23 S25 Introduction to ARCH and GARCH

Lecture 24 S26 Modeling Volatility in Stata

Section 8: Unit 8 Structural Breaks

Lecture 25 S28 Introduction to Structural Breaks

Lecture 26 S29 Detecting Structural Breaks

Section 9: Unit 9 Panel VAR and Cointegration

Lecture 27 S30 Introduction to PVAR and Panel Cointegration

Lecture 28 S31 PVARs in Stata

Section 10: Unit 10 Next Steps

Lecture 29 S33 Your Journey

If you are interested in time series modeling and forecasting, this course is for you.