EViews - Modelling: Complex Models
EViews - Modelling: Complex Models
This two-day-course is an advanced training for the analysis of economic time series data. We start with a recap of basic time series analysis and dynamic models in form of linear regression and ARIMA-models.
As a generalisation of that multivariate regression models and systems of equations are discussed. Vector-Autoregressive (VAR) and Vector-Error-Correction-Models (VEC) are part of the training as a special case of systems of equations.
Final part of the training describes ways to model volatility of time series in form of Autoregressive Conditional Heteroscedasticity-models (ARCH, GARCH).
Content:
- AR, MA and ARIMA models
- ARDL - Dynamic regression models
- Systems of Equations
- VAR- and VEC-models
- ARCH- and GARCH-models
Requirements:
Participants should have a basic understanding of hypothesis testing, regression analysis and time series analysis in general. These topics are covered in our training Learning Econometrics with EViews.
EViews Modeling
In this course we focus on advanced methods for modeling in econometrics. After a review of time series analysis we turn our attention to dynamic regression models that consider the dynamic structure of economic processes. Then we present the simultaneous equation models that allow to deal with several variables and interactions in parallel. This naturally leads to vector models, like VAR (vector autoregressive) and VEC (vector error correction) models. Finally we introduce ARCH and GARCH models for the analysis of financial markets.
Objectives:
- AR, MA, ARMA, ARIMA models
- ARDL - dynamic modelling
- simultaneous equation models
- VAR and VEC models
- ARCH and GARCH model