The Econometrics of Macroeconomic and Financial Data

Last week I received the full published special issue of Journal of Econometrics, 231(2), 2022 (The Econometrics of Macroeconomic and Financial Data). I am deeply grateful and humbled. What a wonderful gesture. Heartfelt thanks to the J. Econometrics Editorial Board, and to all the students, co-authors, and colleagues who contributed. Special thanks to Atsushi Inoue, Lutz Kilian and Andrew Patton for their thoughtful introduction and meticulous editing, and for so generously attempting (twice) to host the associated 60th birthday conference. Clearly COVID did not defeat us!

Moral Hazard in Climate Change Adaptation

Fascinating color on sea level rise in Jakarta, and good insight into the moral hazard associated with certain types of adaptation. 

 https://allanhsiao.github.io/files/Hsiao_jakarta.pdf

https://allanhsiao.github.io/

Abstract:  Sea level rise poses an existential threat to Jakarta, which faces frequent and worsening flooding. The government has responded with a proposed sea wall. In this setting, I study how government intervention complicates long-run adaptation to climate change. I show that government intervention creates coastal moral hazard, and I quantify this force with a dynamic spatial model in which developers and residents act with flood risk in mind. I find that moral hazard generates severe lock-in and limits migration inland, even over the long run.

Machine Learning and Central Banking

 Of course machine learning (ML) is everywhere now.  The time-series analysis perspective has matched that of ML for decades (parsimonious predictive modeling allowing for misspecification; out-of-sample evaluation; ensemble averaging; etc.), so there are many areas of overlap even if there are also many differences.

It's interesting to see ML emerging as particularly useful in central banking contexts.  The Federal Reserve Bank of Philadelphia, for example, now explicitly recruits and hires "Machine Learning Economists".  Presently they have three, and they're looking for a fourth!

In that regard it's especially interesting to learn of a call for papers for a special themed issue of Journal of Econometrics on "Machine Learning for Economic Policy", with guest editors from a variety of leading central banks and universities.

See https://www.bankofengland.co.uk/events/2022/october/call-for-papers-machine-learning-for-economic-policy and below.

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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 policy makers 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.

The Latest in Observation-Driven TVP Models

Check this out.  The implicit stochastic-gradient update seems very appealing relative to the "standard" GAS/DCS explicit update.

"Robust Observation-Driven Models Using Proximal-Parameter Updates", by Rutger-Jan Lange, Bram van Os, and Dick van Dijk.

https://www.tinbergen.nl/discussion-paper/6188/22-066-iii-robust-observation-driven-models-using-proximal-parameter-updates