Appreciation to Dr. Jurgen Doornik (University of Oxford)

I would like to express my appreciation to Dr. Jurgen DOORNIK (University of Oxford) for providing a seminar on Automatic Selection of Multivariate Dynamic Econometric Models and a workshop on Automatic Model Selection with Applications.

Thanks Dr. Jurgen DOORNIK for his contribution to the “IIDS Project – Recent Developments in Theoretical and Applied Econometrics Analysis”. We are looking forward to our research collaborations in the future.

Project Website: https://ecme.hksyu.edu/index.php/category/model-selection/

Workshop : Automatic Model Selection with Applications

Speaker:  Dr. Jurgen DOORNIK (University of Oxford)

Date:       26 June 2019 (Wed)

Time:       11:00 am – 1:00 pm

Venue:     RLG402, Research Complex

Abstract:  Automatic model selection is a powerful tool for the empirical modeller. This workshop will introduce Autometrics, which successfully implements the general-to-specific approach. Foundations of the algorithm will be described, together with interesting extensions, including applications that have more variables than observations. Hands-on computer illustrations will be used throughout.

A modeller is confronted with:

  • many possible inter-related variables that matter,
  • subject to intermittent breaks (earthquakes, financial crises, etc.),
  • possible outliers (oil shocks, tsunamies, etc.)
  • changes to the measurement system
  • and dramatic changes over longer periods (recessions,technological progress, climate change)

Big data may help, but can bring further problems:

  • data overload,
  • too much heterogeneity,
  • hidden stratification and unknown dependence.
  • Information overload and complexity

Automatic modelling essential to cope with these challenges. Autometrics is the implementation of automatic model selection, designed to handle these challenges:

  • Automatic: computer is powerful modelling aid,
  • General to specific: can maintain econometric properties,
  • Extensive search: to handle correlated data,
  • Efficient search: need to estimate many models,
  • Statistical congruence: maintained as a search constraint,
  • Statistical properties: extensively researched,
  • Not maximizing goodness-of-fit: avoids overfitting,
  • Controlled by gauge: expected number of falsely selected variables,
  • Flexible: more variables than observations, different model classes (logit, SEM), …
XlModeler brings regression and volatility models of OxMetrics to Excel

I am impressed by the power of XIModeler to estimate 300 models and select the best model within 1 sec automatically.

Rsources:

XIMODELER: https://www.xlmodeler.com/

An introduction to XImodeler:https://www.xlmodeler.com/doc/xlmodeler.pdf

Oxmetrics: is a family of of software packages providing an integrated solution for the econometric analysis of time series, forecasting, financial econometric modelling, or statistical analysis of cross-section and panel data. OxMetrics consists of a front-end program called OxMetrics, and individual application modules such as PcGive, STAMP, etc. Oxmetrics

Seminar: Automatic Selection of Multivariate Dynamic Econometric Models

Speaker:  Dr. Jurgen DOORNIK (University of Oxford)

Date:       26 June 2019 (Wed)

Time:       3:30pm – 5:30pm

Venue:     RLB303, Research Complex

Abstract:  Automatic general-to-specific selection of univariate econometric models is now well established and available in software. Extensions include saturation estimators, e.g. adding an impulse dummy for every observation to handle outliers. This seminar will provide an overview of the approach, and then consider extension of these procedures to the multivariate setting. The starting point is a vector autoregression, and the final stage can be a simultaneous equations model where the role of identification is considered. The aim is to obtain procedures that are relevant for empirical modelling.

The need for machine-assisted learning in econometrics:

  • Developing good models is difficult.
  • Working with economic data is difficult:
  • approximate measurements subject to revisions on a system that is huge,
  • evolving, intercorrelated, maybe nonlinear, and prone to abrupt shifts.
  • Need models for policy as well as forecasting :
  • Black-box models insufficient: need to understand,
  • Nonlinearities of secondary importance.
  • Proliferation of data: Big data:
  • But is there a proliferation of insight?

General-to-specific model selection (Gets, ‘Hendry’ or ‘LSE’ methodology) largely driven by David Hendry (DHSY, PcGive, Alchemy, Dynamic Econometrics, …)

General-to-specific automatic model selection, developed methodology and algorithms to handle these challenges

Resources:

Doornik, J. A. (2009). Autometrics.
In J. L. Castle and N. Shephard (Eds.), The Methodology and Practice of Econometrics: Festschrift in Honour of David F. Hendry. Oxford: Oxford University Press.

Empirical Model Discovery and Theory Evaluation Automatic Selection Methods in Econometrics By David F. Hendry and Jurgen A. Doornik
Published by the MIT Press

Doornik, J. A. and K. Juselius (2018).
CATS 3: Cointegration Analysis of Time Series in OxMetrics.
London: Timberlake Consultants Press.

Photo Gallery