Econometric Learning & Nonlinear Econometric Methods

This is the joint website of the research projects “New Nonlinear Econometric Methods with Macroeconomic and Financial Applications” (Spring 2019 - 2021) financed by the Emil Aaltonen Foundation and “Econometric Learning Methods for Macroeconomic and Financial Applications” (September 2019 - 2023) financed by the Academy of Finland.

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 research assistants, some of them to be recruited in the autumn 2019.

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.


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).

Ongoing research (since the start of the Aaltonen Foundation project in the spring 2019)

Discussion/working paper versions on these topics, and few others, will be available shortly.

Lof, M., and H. Nyberg (2019). Discount rates and cash flows: A local projection approach. SSRN working paper (abstract_id=3372138)  

Kauppi H., and H. Nyberg (2019). Semiparametric selection and optimal weighting of leading economic indicators.

Nyberg, H. (2019). Taking zero lower bound seriously: A structural vector autoregression containing positive-valued components. 


“Discount rates and cash flows: A local projection approach”

* Nordic Econometric Meeting (Stockholm, 2019)

* EEA-ESEM 2019 (Manchester, 2019)


Other activities

* Nowcasting seminar at the University of Turku (Turku Center of Statistics)

* Organized session "Statistical learning in macroeconomics and finance" at the 13th International Conference on Computational and Financial Econometrics (CFE 2019), London, December 2019

See also my (Nyberg) past research topics and connections to various research departments also around these projects.