Estimation Methods in Econometrics using EViews

Estimation Methods in Econometrics using EViews

Inscreva-se aqui
Inscreva-se aqui
190,00 €
Pagamento seguro e garantido
–
1 Day
Online
EViews

Overview

Unlock the power of modern estimation techniques for linear models—whether working with a few variables or handling complex, high-dimensional data. This hands-on course introduces three essential methods:

  • Quantile Regression with Least Absolute Deviations (LAD): Gain insights beyond the mean by modeling different parts of the distribution.
  • Robust Least Squares: Detect and control for outliers to improve model reliability.
  • Elastic Net Techniques (Ridge, LASSO, and Elastic Nets): Master variable selection and regularization for high-dimensional models.
How It Works
What You’ll Learn

Understanding Non-Stationarity and Unit Roots
  • The importance of stationarity in time series analysis.
  • Detecting unit roots using the Dickey-Fuller test in EViews.
Cointegration and Long-Run Relationships
  • The concept of cointegration and its significance in modeling economic relationships.
  • Implementing the Engle-Granger two-step method in EViews.
Multivariate Cointegration and the VECM
  • Johansen’s test for identifying cointegration in multivariate time series.
  • Introduction to the Vector Error Correction Model (VECM) and its applications.
Practical Hands-On Session
  • Applying VECM techniques to real-world datasets using EViews.
  • Interpreting results and making data-driven decisions.
Why This Course?

Through interactive sessions and practical exercises in EViews, you'll build the confidence to apply these techniques to real-world datasets. By the end of the course, you'll be fully equipped to estimate and interpret linear models with precision, no matter the data complexity.

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

Brief revision of OLS:

  • Definition of the estimator

  • An example with EViews

rogramming and Series Transformations:

  • The notion of Quantile Regression

  • The Least Absolute Deviation estimator

  • An example with EViews

Robust Least squares:

  • Robust estimation and outliers

  • The M-estimator

  • The double-M-estimator

  • Examples with EViews

Elastic Nets:

  • Introduction to penalised estimation for small and large models

  • The Ridge estimator: implementation, cross-validation and inference

  • The Lasso: implementation, cross-validation, variable selection and inference

  • Convex combinations of Ridge and Lasso: elastic nets and their properties

  • Examples with 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

  • No prior knowledge of EViews required
  • Basic Regression and Statistics knowledge

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.

Associados Acadêmicos

Student Testimonials