Score-driven TEnsor Autoregressive DYnamical models

Project Information

STEADY

Grant agreement ID: 887220

DOI

10.3030/887220

Project terminated on 31 August 2022

EC signature date 30 April 2020

Start date 1 October 2020

End date 30 September 2022

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 175 572,48

EU contribution

€ 175 572,48

Coordinated by

STICHTING VU
Netherlands

Periodic Reporting for period 1 - STEADY (Score-driven TEnsor Autoregressive DYnamical models)

Reporting period: 2020-10-01 to 2022-09-30

Summary of the context and overall objectives of the project

Modern economic analyses require new models to study increasingly fine-grained interrelations based on increasingly complex data sources. Early dynamic economic analyses have mostly been limited to only studying univariate time series, which can be represented as a single sequence (or vector) of values. Most contemporary analyses use more complicated data with both time series and cross-sectional dimensions, such as panels of key macroeconomic figures, for many countries over time. Such data can be represented as a (2-dim) matrix.

Recently, more complex data structures have rapidly emerged, requiring higher dimensional storage objects. As an example, a data set consisting of a time series (1st dimension) of the exposures of banks (2nd dim) to other banks (3rd dim) in several markets (bonds, equity; 4th dim) and for different maturities (5th dim). The storage object for such high-dimensional data sets is a generalization of a matrix, called a tensor. Models for tensor data have applications to policy-relevant questions for central banks and financial regulators, including forecasting multi-country, multi-market interest rate term structures for the evaluation of monetary policy effectiveness, and nowcasting multi-country economic activity in the heterogeneous European context.

Tensor data are highly topical, however, in econometrics their use and the development of tensor models is very scant and almost exclusively limited to static tensors. The STEADY project fills this gap by developing novel statistical methods for time series of tensor data that account for the typical non-linear and dynamic features of economic data in a computationally feasible way. The project has two main research directions. One is the development of a general class of dynamic time-series models, which merge the linear tensor time series literature and the score-driven time-varying parameter approach based on the Generalized Autoregressive Score (GAS) model. The other contribution consists in the development of a new tensor-based compression technique for many economic time series, the tensor dynamic factor model.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The STEADY project has resulted in several successful publications and presentations in line with the original proposal. As envisaged/hoped for, this includes publications in some of the highest end academic journals like in my field, such as the Journal of the American Statistical Association, the Journal of Business Economic Statistics, and the Journal of the Royal Statistical Society.
Regarding the different audiences to be reached, I was successful in disseminating the outputs of the project to the international academic community (see the conferences and seminars in Section 1.2.6). Instead, due to the COVID-19 cancelling my secondment at ECB, I was prevented having the maximum potential impact on the policy circles. Finally, as the broad audience is concerned, it is interesting to note that several papers were (re)-tweeted on social media, such as
• Publication 2 (https://www.altmetric.com/details/111345118).
• Publication 4 (https://www.altmetric.com/details/111345112/twitter).
• Publication 7 (https://www.altmetric.com/details/97798815/twitter).
• Publication 8 (https://www.altmetric.com/details/105829140).
In addition, and in line with the proposed STEADY project, for the large public I have created a publicly accessible website (https://matteoiacopini.github.io/STEADY.html) that collects the details and output of the project.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The expected results are:

1. Publications: the research output will be published on peer-reviewed international journals (e.g. Journal of Econometrics, Journal of the American Statistical Association, Journal of Business Economic Statistics, etc.),
using also open-access options wherever possible (a specific part of the budget will be allocated to this). Working papers will be made quickly available online via the major repositories (e.g. SSRN, RePEc, arXiv).

2. Code availability: the computer code will be made publicly available on the dedicated website (STEADY.eu) for allowing other researchers to implement the proposed methods. If the results will show a high level of innovation
and a very promising performance, we will consider the creation and provision of a service for policymakers.

3. Conferences: research outputs will be presented to a scientific audience at international conferences and workshops (e.g. SoFiE, CFE-ECRIM, EC2, EMS, ISBA, etc.) and to a policy audience by means of internal
seminars and specialised workshops (e.g. workshops at De Nederlandsche Bank, ECB, etc.). Internal seminars at VUA, TI and ECB will be held periodically for monitoring the progress of my research, local networking and
increase the opportunities for adoption of the findings and for further collaborations.

4. Interaction with policymakers and regulators: the interaction with policymakers will primarily take place during the secondment period at ECB, where I will organize bi-monthly meetings or seminars targeted to policy
audiences, to show the progresses of STEADY and the potential uses of its outcomes for policymaking. The informal networks that will be developed at ECB are instrumental for further dissemination.

5. Other Dissemination: I will co-supervise PhD and Master students in my research areas. Furthermore, I will disseminate key findings via dedicated website (STEADY.eu) newsletter and social media.

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