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Theory and practice of modelling stationary data: ARMA and VAR models

Theory and practice of modelling stationary data: ARMA and VAR models

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£160.00
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1 Day
Online
EViews

Overview

Time series modelling is a critical skill for understanding and forecasting macroeconomic variables, particularly when dealing with stationary processes.

 

This one-day online course provides a practical introduction to modelling stationary time series using EViews, with a focus on ARMA and VAR models. Participants will explore key concepts such as stationarity, unit root testing, and model estimation through hands-on exercises and real-world examples.

 

With applications including inflation forecasting, the course equips attendees with the tools to implement and interpret atheoretical models confidently. Ideal for those looking to enhance their forecasting skills and apply time series techniques effectively in practice.

 

How It Works
What You’ll Learn

  • The structure and components of ARMA (Autoregressive Moving Average) models

  • How to apply the Box-Jenkins identification procedure for time series modelling

  • Techniques for handling trends and seasonality in stationary data

  • Methods for unit root testing and achieving stationarity using differencing

  • Implementation of ARMA models for univariate forecasting in EViews

  • Evaluation of forecast accuracy and performance metrics

  • Estimation and interpretation of stationary VAR (Vector Autoregressive) models

  • Application of Granger causality tests within VAR frameworks

  • Lag length selection and its impact on VAR model quality

  • Practical forecasting using VAR models in EViews with real-world macroeconomic data

Why This Course?

This course is designed for professionals and researchers who want to deepen their expertise in modelling stationary time series data using EViews. By combining theory with practical applications, it offers a hands-on pathway to mastering ARMA and VAR models, with a special focus on macroeconomic forecasting. Whether you're aiming to enhance your analytical toolkit or improve your forecasting accuracy, this course provides the essential techniques and confidence to apply them effectively.

 

Course Highlights
  • Comprehensive Coverage: From fundamental concepts to advanced dynamic models.
  • Practical Learning: Real-world case studies and hands-on exercises with Stata.
  • Expert Insights: Gain clarity on complex topics like endogeneity and serial correlation.
  • Interactive Format: Live Q&A sessions to address individual questions and challenges.

 

Who Should Attend?

By the end of the course, participants will have a solid foundation in modelling non-stationary variables, equipping them with the skills needed to analyze complex time series data effectively.

Agenda

Non-Stationarity I - Unit Roots

Session 1: Statistical Analysis of Time Series 

1.1 Definitions: 

  • Definition and components of Autoregressive Moving Average (ARMA) models. 
  • Treatment of trends and seasonality. 
  • Application of filters in time series analysis. 

 

1.2 Stationarity and Non-Stationarity: 

  • Review of stationarity and non-stationarity in time series. 
  • Introduction to unit roots and their significance. 
  • Unit root testing in EViews. 
  • Series differencing as a technique for achieving stationarity. 
Session 2: Univariate Forecasting I 
  • Forecasting with ARMA Models
  • Implementation of ARMA models for univariate forecasting. 
  • Evaluation and measurement of forecasting accuracy. 
Session 3: Atheoretical Models II: Stationary VARs 

3.1 VAR Representation and Estimation: 

  • Introduction to Vector Autoregression (VAR) models.
  • Procedures for VAR representation and estimation in EViews. 

 

3.2 Further Testing with Multivariate Regression: 

  • Granger causality testing within the framework of VAR models. 
  • Lag selection methods to enhance VAR model performance. 
Session 4: Atheoretical Models III - Stationary VARs 
  • Forecasting with VARs: 
  • Application of VAR models for forecasting in a stationary context. 
  • Practical considerations and techniques for VAR forecasting in EViews. 
Day 2:

Session 1
  • Comparing estimators for static panel models for your research question 
  • Testing for serial correlation
Session 2: Dynamic Panel Models
  • The Arello Bond estimator and post-estimation diagnostic test 
  • The Blundell Bond estimator and post estimation diagnostic tests
  • Case study: the determinants of bank risk-taking in European banks.

Prerequisites

Previous knowledge of regression analysis is highly desirable; some knowledge of models for stationary time series (chiefly, ARMA models) would also be beneficial.

Subject to minor changes

Day

Morning Session

Afternoon Session

Day One

10am-2pm (London time)

2pm-4pm (London time)

Terms

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Temporary, time limited licences for the software(s)  used in the course will be provided. You are required to install the software provided prior to the start of the course.
  • Payment of course fees required prior to the course start date.
  • Registration closes 1-calendar day prior to the start of the course.

 

  • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
  • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
  • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

 

The number of attendees is restricted. Please register early to guarantee your place.

Delivered By

Student Testimonials

Giovanni's delivery is fantastic; makes great connections between new and prior knowledge and focuses on the key strengths and limitations of the discussed methods. Excellent course design that builds on the Introductory Machine Learning course and knowledge acquired in the PhD Econometrics sequences of courses. This is all nicely supplemented by detailed Stata code with explanations and sample datasets. 

Excellent course and great explanations on ML techniques and applications from Giovanni ! I leanred so much including the coding and applications plus the fundamentals of ML.

The 'Advanced Machine Learning (AML)' experience was excellent for trying to gain more experience in Statistics using links Python and STATA.  

I'm not a Statistician! However, Giovanni managed to link the 'Fundamentals of Machine Learning (FML) ' to 'Advanced Machine Learning' in his usual excellent way. When starting the AML, for me I am pleased that the FML was a tremendous help and allowed me to use my mathematical knowledge for Physics and Science. I'm looking forward to Giovanni's next course (using large datasets) and his book.

Linking my knowledge of mathematics (from Science and Engineering) to Statistics. I do hope it is leading towards becoming better at 'Medical Statistics' that require very large datasets...and a big thank you to Giovanni!

Very well organized, very useful and relevant content, looking forward to joining future events!

As always great service and real good courses. In addition, thanks to Professor Cerulli for making himself understood in the best way.

The delivery of this course was exceptionally well done. It really helped me to appreciate the concepts as well as the practical applications in Stata. If you are new to this topic, this will provide a good introduction to complex issues.

Very easy to communicate, all emails contained all the information necessary. I think that the course was very well structured and organized. The tutor provided a number of codes that were extremely helpful for understanding. Overall, very useful and easy to follow!

I highly appreciated Professor Giovannu Cerulli course. The classes notes are very clear   and well prepared with an extensive coverage of the course subjects. And they are simultanesouly quite objective by focusing on the most important contents. Professor Giovannu Cerulli lectures are very didatic which greately helps the easily assimilation of the   corespondent knowledge. Furthermore, the course materials are quite   comprehensive and they englobe not only the classes notes, but also the referenced papers as well as data and Stata programs to estimate the models in this software. All in all, I greatly recommend this   course, as it really amazingly speeds up the acquaintance of the underlying theory and appied aplication in a very short period of time.

I found the Stata Summer School 2021 very useful and interesting. The course was perfectly structured and organised, with a good progression during the week. The instructors presented the topics covered in an easy and understandable way. There were room for questions and answers when needed. Materials shared for the course were tidy and informative, and I am sure I will use them frequently. This course was arranged online, which in my opinion worked very well. I believe the course delivered as promised and according to information found online when I signed up for the course. Easy to purchase/sign up for the course. User friendly. Quick and timely response.

Very efficient in terms of communication and delivery. Provides a very comprehesnive applied knowledge of stata. I would definitely recommend others to buy from them.

I went UK University of Cambridge for a summer school with Timberlake, it was excellent.

It was a great course and I thoroughly enjoyed it. Many of my fellow participants were eager to share their ideas. I thought the course could help further many people in a similar stage to my career!