Delivered By: Prof. Christophe Hurlin
Course Overview
The objective of the course is to learn how to model an ARDL and to understand the advantages of this model for the implementation of forecasting models.
At the end of the course, participants will be able to implement cointegration tests in the framework of ARDL models by respecting a rigorous specification approach, to derive an ECM representation of an ARDL, to interpret it economically, and to build forecasts using an ARDL model.
They will also implement the full specification and validation tests of the ARDL model forecasts available in Eviews.
Course Context
The ARDL (Auto-Regressive Distributed Lag) models were introduced by Pesaran and Shin (1998) and Pesaran, Shin and Smith (2001). ARDL are dynamic time series models that are particularly well suited to projection exercises and banking stress tests. These models have several advantages when it comes to build a forecasting model for a microeconomic series (loss rate, default rate of a credit portfolio, etc.) using macroeconomic series aggregated at the national or sectoral level.
Advantages of ARDL Models:
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ARDL as a re-parameterization of Conditional Error Correction Model (CECM) derived from a Vector Auto-Regressive (VAR) representation.
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Ability to model the dynamics of credit portfolio metrics as a function of exogenous macroeconomic variables.
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Robust handling of cointegration, allowing for a mix of I(0) and I(1) variables without prior testing of stationarity.
Course Objectives:
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Learn ARDL modeling and understand its advantages for forecasting in stress test scenarios.
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Implement cointegration tests within ARDL frameworks.
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Derive and interpret an Error Correction Model (ECM) from an ARDL.
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Build forecasts using ARDL models, including full specification and validation tests.
Course Agenda
Day 1
Morning session:
- 3 hours.
- Introduction to ARDL models.
- Advantages of ARDL models for stress test modelling.
- ARDL model specification.
- Estimation of ARDL parameters.
- Selection of the ARDL delay structure.
- Rewriting an ARDL in ECM form.
- Economic interpretation and calculation of long-term multipliers.
Afternoon session:
- 3 hours.
- Reminder: Non-stationarity, cointegration and spurious regression.
- Cointegration tests: F-bounds tests.
- Complementary tests: t-bounds test.
- Specification of the deterministic component of the model.
- Specification strategy for ARDL models.
Day 2
Morning session:
- 3 hours.
- Construction of a stress test model by ARDL approach.
- Reminder on unit root and cointegration tests.
- Practical application of the estimation procedure of an ARDL model on different databases reflecting the different configurations of the model.
Afternoon session:
- 3 hours.
- Build a forecast with an ARDL model.
- In-sample evaluation.
- Model validation tests.
- Analysis of autocorrelation and heteroscedasticity of residuals.
- Specification tests for ARDL model.
- Stability tests of the forecast.
Prerequisites
- Basic knowledge of econometrics and time series (ARMA, VAR, non-stationarity and cointegration models). Reminders on these notions are provided as part of the training.
- Basic knowledge of the EViews software
Principal texts for pre-course reading:
- Time Series Data Analysis Using Eviews, Gusti Ngurah Agun, Wiley (2011).
- Pesaran, M. H. and Shin, Y. (1998). An autoregressive distributed-lag modelling approach to cointegration analysis. Econometric Society Monographs, 31:371-413.
- Pesaran, M. H., Shin, Y., and Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3):289.326.
Terms & Conditions
- 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.
- Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course.
- Payment of course fees required prior to the course start date.
- Registration closes 5-calendar days 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 delegates is restricted. Please register early to guarantee your place.