Research: New econometric learning & nonlinear methods for financial and macroeconomic data

This is the joint website of three ongoing research projects running at Statistics (Department of Mathematics and Statistics) and Economics (Turku School of Economics), headed by Associate Professor Henri Nyberg and Post-doc researcher Kim Ristolainen

Research team/members

Visa Kuntze (project researcher, Statistics)

Niko Lietzén (researcher, Statistics)

Lauri Nevasalmi (researcher, Statistics)

Tomi Roukka (researcher, Economics)

The projects, with links to details, are the following:

“Econometric Learning Methods for Macroeconomic and Financial Applications” (Autumn 2019 – 2023), financed by the Academy of Finland (PI: Nyberg)

“New Nonlinear Econometric Methods with Macroeconomic and Financial Applications” (2019 – 2021), financed by the Emil Aaltonen Foundation (PI: Nyberg)

This Time is Different – Syndrome, excess risk-taking and financial crises: empirical approach utilizing artificial intelligence and text data” (2020 – 2022), financed by the Emil Aaltonen Foundation (PI: Ristolainen)

The research started in the spring 2019 (Emil Aaltonen Foundation project), whereas the Academy project (for young researchers) is starting in the autumn 2019. The personnel will include post-doc researchers, PhD students and currently two research assistants (Elsi Lindell and Miko Pasanen).

The goals of these projects are related to the development of new econometric and statistical methods. The expected methodological development offers new ways of econometric analysis of economic and financial time series. The expected empirical results shed light on the behavior of macroeconomy and financial markets, valuable for various decision makers and practitioners, and having potential for new consequences to the theoretical economic and financial models.

To successfully examine the new ideas, these multidisciplinary projects call for expertise in econometrics, statistics (including time series analysis and machine and statistical learning) and economics/finance. The aim is to address, for their parts, various general gaps in knowledge in the state-of-the-art of econometric research, such as:

* Lack of decision-making perspective: We aim to tighten the decisionmaking perspective and the final use of forecasts more explicitly to the bases of the method such as the objective/loss function.

* Underestimation of the impact of turning points and directional predictive perspective: We put more effort on getting directional predictions correct when things really matter, such as around the turning points in business and financial cycles and financial crises.

* Neglected bounded dynamics: The idea is to respect the natural bounds in variables, such as the recent Zero Lower Bound (ZLB) in nominal interest rates, generally implying a need for new nonlinear methods.

* Lack of nonlinear and non-Gaussian structural econometric tools: As a part of methodological development to develop nonlinear structural econometric methods, such as the ones related to impulse response analysis, to facilitate economic interpretations.

* Text as data and overconfidence: The idea is to measure overconfidence from historical newspaper articles by using so-called deep learning models.

The projects are connected but aiming to answer the above challenges from somewhat different perspectives and with different methods:

New nonlinear econometric methods (Emil Aaltonen Foundation project)

A characteristic joint feature in the above challenges is the limited dependence (i.e. the objective of interest is not necessarily continuous/real number). Thus, limited dependent econometric methods (i.e. generalized linear models) in time series context are generally required to address them. A part of this contribution is to integrate classification-based thinking to common modelling and forecasting problems, i.e. to introduce nonlinear methods based on their directional and economic performance instead of the traditional statistical criteria such as the least squares.

Econometric learning (Academy of Finland)

In addition to the above contributions related mainly to the conventional type of econometric methods, the general idea in this project is to develop “econometric learning” concept by integrating increasingly popular and up-to-date machine and statistical learning methods with the economic theory and practice. That is to develop learning-based econometric methods addressing the specific needs and characteristics of macroeconomic and financial applications. The econometric learning methods will be largely driven by their use in forecasting purposes, addressing the required renewal of econometric analysis in modern data-rich environments. In addition to forecasting objectives as such, the new methods are partly based on the nexus between econometric forecasting and the final use of forecasts in decision making that has so far been widely overlooked (as mentioned above).

Text as data (Emil Aaltonen Foundation project)

Purpose of the project is to utilize machine learning – especially deep learning models – and text data from historical newspapers to form important variables needed in economic research for longer time samples that have usually been available for economists. A special focus will be given to general overconfidence and the possible relationship it has with various economic phenomena such as banks’ risk-taking and further on to financial crises.

Publications, ongoing research papers and activities (2019 – present )

Discussion/working papers and given presentations

* Kauppi H., and H. Nyberg (2020). Optimal selection and weighting of leading economic indicators.

Presentations: Helsinki GSE (2019), ACE (University of Turku, 2019), CFE 2019 (London)

*Lanne, M., and H. Nyberg (2020). Nearest neighbours-based impulse response analysis: U. S. government spending multipliers in nonparametric world. Work in progress

Presentation: Helsinki GSE (2020), Melbourne Online Series of Econometric Seminars (2020)

* Lof, M., and H. Nyberg (2019). Discount rates and cash flows: A local projection approach. SSRN working paper (id3372138)

Presentations: Nordic Econometric Meeting 2019 (Stockholm), EEA-ESEM 2019 (Manchester), Paris December Finance Meeting 2019, ASSA 2020 Annual Meeting (San Diego) , European Finance Assocation (EFA) 2020 Annual Meeting (Helsinki), SNDE 2020 (Annual Symposium of the Society for Nonlinear Dynamics and Econometrics, Virtual event) (University of Zagreb)

* Nevasalmi, L. (2020). Forecasting multinomial stock returns using machine learning methods. SSRN working paper (id3630222)

Presentation: CFE 2019 (London)

* Nevasalmi, L. (2020). Recession forecasting with big data. SSRN working paper (id3630146)

* Nevasalmi, L., and H. Nyberg (2020). Moving Forward from Predictive Regressions: Boosting Asset Allocation Decisions. SSRN working paper (id3623956)

Presentation: Turku Finance Workshop 2019

* Nyberg, H. (2020). Risk-Return Relation in Stock Returns under Economic Constraints.  Work in progress

Presentation: eMAF 2020 (Mathematical and Statistical Methods for Actuarial Sciences and Finance)

* Nyberg, H. (2020). Taking zero lower bound seriously: A structural vector autoregression containing positive-valued components.  Work in progress

Domestic publications

* Juvonen, P., Anttonen, J., Fornaro, P., Nissilä, W., Nyberg, H., and H. Pönkä). Aikasarjamallit apuna Suomen talouden seurannassa The Finnish Economic Journal (Kansantaloudellinen aikakauskirja), 115 (3), 440 – 457

Other activities

* Nowcasting seminar at the University of Turku (Turku Center of Statistics, August 2019

* Organized session “Statistical learning in macroeconomics and finance” at the13th International Conference on Computational and Financial Econometrics (CFE 2019), London, December 2019

Societal interaction

* Opinion (article, in Finnish): Ristolainen: Väitteet pankkien liiallisesta sääntelystä ovat harhaanjohtavia. Kauppalehti 23.1.2020

*Special seminar: Tilastotieteen ja tekoälyn tarjoamat mahdollisuudet vedonlyöntisijoittamisessa ja urheilun mallintamisessa (“Statistics and artificial intelligence in sports betting and modelling sports”) Turku Center of Statistics 13.2.2020