A themed issue in the Journal of Econometrics invites paper submissions on “Machine learning for economic policy” by May 31, 2023.
About this themed issue
This themed issue in the Journal of Econometrics is jointly organized by major policy institutions and universities. It aims to cover a diverse set of problems where machine learning approaches and novel data sources are applied to situations, which are relevant to economic policy making.
It will cover a range of applications and methodological contributions such as deep learning, text analytics, reinforcement learning, shock identification, forecasting and nowcasting, identification, as well as different approaches to model interpretability and inference, among others.
Machine learning techniques are increasingly being evaluated in the academic community and at the same time leveraged by practitioners at policy institutions, like central banks or governments. A themed issue in the Journal of Econometrics aims to present frontier research that sits at the intersection of machine learning and economic policy.
There are good reasons for policymakers to embrace these new techniques. Tree-based models or artificial neural networks, often in conjunction with novel and rich data sources, like text or high-frequency indicators, can provide prediction accuracy and information that standard models cannot. For example, machine learning can uncover potentially unknown but important nonlinearities within in the data generating process. Moreover, natural language processing—made possible by advances in machine learning—is increasingly being applied to better understand the economic landscape that policymakers must survey.
These upsides of these new techniques come with the downside that it often is not clear what the mechanism through which the machine learning model operates, i.e., the black box critique. Much of the existence of the black box critique is due to how machine learning models evolved with a focus on accuracy. However, this single focus can be particularly problematic in decision making situations, where all stakeholders have an interest in understanding all pieces of information which enter the decision-making process, irrespective of model accuracy. The tools of economics and econometrics can help to address this problem thereby building bridges between disciplines.
It is crucial that the policy dimension of accepted papers is significant and integral to the contribution of each paper. Therefore, it is not sufficient that the policy relevance of these papers is restricted to an empirical illustration. Applications, case studies, or experiments should show a clear way how the insights derived from them can help economic decision makers.
If your paper covers both the methodological and policy angle outlined above, we invite you to submit your paper to the Journal of Econometrics themed issue “Machine Learning for Economic Policy  [IG000583]”.
The deadline for submission is May 31, 2023. Submissions will be processed as they arrive.
Handling editor, Journal of Econometrics:
- Serena Ng (Columbia University)
Guest associated editors:
- Maryam Haghighi (Bank of Canada)
- Andreas Joseph (Bank of England)
- George Kapetanios (King’s College London)
- Christopher Kurz (Federal Reserve Board)
- Michele Lenza (European Central Bank)
- Juri Marcucci (Bank of Italy)