Inference for High-Dimensional Econometric Time Series

Summary

I develop econometric methods for inference on high-dimensional time series data. Nowadays large and complex datasets are available in economics that allow for deep analysis of large amounts of information relevant to understand economic developments. World-wide panels of macroeconomic data, large disaggregate datasets on country-level, high-frequency financial data and series derived from electronic sources such as Google Trends or social media all provide opportunities for improving economic forecasting and policy analysis. Such datasets typically contain large numbers of potential predictors, requiring statistical methods specifically designed for high-dimensional models with many parameters to estimate. While development of such techniques has been flourishing in the field of statistical learning, most existing methods are not geared towards econometric time series and therefore unsuitable for datasets such as described above.

I develop penalized regression methods for estimation, prediction and, in particular, uncertainty quantification for high-dimensional time series, allowing for complex dependencies, persistency and trends as typically observed in economic time series. I develop honest methods of inference, which explicitly take uncertainty arising from not knowing the true model into account when conducting inference. This avoids underestimating the true estimation (and model) uncertainty that occurs when model or variable selection is ignored. In addition, I develop bootstrap methods for uncertainty quantification in high-dimensional time series analysis, which are not only justified theoretically, but also provide accurate and reliable inference in practice. I also demonstrate the practical suitability of my methods by applying them to high-dimensional economic datasets.
By developing methods that allow for accurate and reliable statistical analysis of complex high-dimensional econometric time series, my project contributes significantly to the tools at the disposal of the modern empirical economist, resulting in improved economic forecasting, policy analysis and general understanding of economic dynamics.

Output

Scientific article

Publications for the general public

  • S Smeekes, L Margaritella, A Hecq(2018): Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure
  • S Smeekes, E Wijler(2018): An automated approach towards sparse single-equation cointegration modelling
  • S Smeekes, A Hecq, L Margaritella(2018): Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure
  • L Margaritella, A Hecq, S Smeekes(2018): Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure
  • E Wijler, S Smeekes(2018): An automated approach towards sparse single-equation cointegration modelling

Details

Project number

452-17-010

Main applicant

Dr. S.J.M. Smeekes

Affiliated with

Maastricht University, School of Business and Economics (SBE), Department of Quantitative Economics

Team members

R. A. Adámek MSc, L Margaritella MSc, Dr. S.J.M. Smeekes

Duration

01/11/2017 to 31/08/2022