The action’s overall goal is to develop new methods for accurate time series analysis of big and complex economic problems. Three specific Research Objectives have been tackled:
1. Honest methods for inference. The action delivers accurate and reliable measures of uncertainty quantification for high-dimensional time series problems. First, bootstrap unit root tests are provided (Smeekes and Wilms, 2020). Unit root test from an essential part of any time series analysis. The software package bootUR provides practitioners with a single, unified framework for comprehensive and reliable unit root testing on single time series or potentially a large number of time series (including panels). Secondly, inferential procedures for large time series models via the desparsified lasso are developed (Adamek, Smeekes and Wilms, 2020).
2. Estimation of Impulse Response Functions. The action delivers novel tools to describe how the economy responds, over time, to unpredictable events (called shocks). As such, policyholders and other decision makers can anticipate outcomes that have not yet occurred. Such tools crucially hinge on accurate estimation procedures for high-dimensional time series models, which have been developed in Barbaglia, Croux, Wilms (2020), Hecq, Ternes, Wilms (2021), Nicholson, Wilms, Bien, Matteson (2020) and Wilms, Rombouts, Croux (2021) and are made publicly available through the software package bigtime. Further extensions towards impulse response analysis based on, for instance, local projections are work-in-progress.
3. Identification strategies for high-dimensional time series models. The action delivers identification strategies that map observed economic data to the relevant economic parameters of interest. Such strategies should be applicable in high-dimensions, favor parsimonious models and integrate identification and estimation strategies via regularization procedures. Wilms, Basu, Bien and Matteson (2020) deliver such strategies for vector autoregressive moving average models; other time series models such as structural vector autoregressive models are currently being studied.
Concerning exploitation and dissemination, several activities have taken place. The work performed during this action has been presented at seminars (Maastricht University 2019; University of York, 2019; Erasmus University Rotterdam, 2020); conferences (Joint Statistical Meetings 2019; CMStatistics 2019; NESG 2020; (EC)^2 2020) and workshops (Big Data and Forecasting Workshop at the Joint Research Centre 2019; StatScale Workshop 2021). The Online Workshop on Dimensionality Reduction and Inference in High-Dimensional Time Series marks the end of the action. Besides, software toolboxes have been made publicly available on CRAN via the R packages bootUR and bigtime.
Publications
Adamek R., Smeekes S. and Wilms I. (2020), Lasso inference for high-dimensional time series, arXiv:2007.10952.
Barbaglia L., Croux C. and Wilms I. (2020), Volatility spillovers in commodity markets: A large t-vector autoregressive approach, Energy Economics, 85, UNSP 104555.
Hecq A., Ternes, M. and Wilms I. (2021), Hierarcical regularizers for mixed-frequency vector autoregressions, arXiv:2102.11780.
Nicholson W.B. Wilms I., Bien J. and Matteson D.S. (2020), High-dimensional forecasting via interepretable vector autoregression, Journal of Machine Learning Research, 21(166), 1-52.
Smeekes S. and Wilms I. (2020), bootUR: An R package for bootstrap unit root tests, arXiv:2007.12249.
Wilms I., Basu S., Bien, J. and Matteson D.S. (2020), Sparse identification and estimation of high-dimensional vector autoregressive moving averages, arXiv:1707.09208.
Wilms I., Rombouts, J. and Croux C. (2021), Multivariate volatility forecasts for stock market indices, International Journal of Forecasting, 37(2), 484-499.
