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Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor. We argue that this method of generating forecasts is inexpensive and can be ...
Journal Article
Interview: Daron Acemoglu
Daron Acemoglu is one of MIT's nine university- wide Institute Professors, the university's highest faculty rank. One of his predecessors, Robert Solow, developed a pathbreaking mathematical model of economic growth in the 1950s. Today, Acemoglu says hurray for economic growth — but is also concerned that choices made by policymakers and companies are channeling the gains from that growth away from workers. And as he sees things, the powerful AI technologies that have come to the fore in the past several years, embedded in products such as ChatGPT, should be regulated with the economic ...
Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor. We argue that this method of generating forecasts is inexpensive and can be ...
Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce in-sample conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor.
Journal Article
Has AI Improved Productivity?
Research Spotlight on "Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform." Erik Brynjolfsson, Xiang Hui, and Meng Liu. National Bureau of Economic Research Working Paper No. 24917, August 2018.
Journal Article
New disruption from artificial intelligence exposes high-skilled workers
With workers still grappling with the consequences of automation, the lightning-speed pace of artificial intelligence (AI) development poses fresh concerns of a new wave of worker displacement.
Journal Article
Federal Reserve: Artificial Intelligence and Bank Supervision
Artificial intelligence has come a long way since English mathematician, logician, and cryptographer Alan Turing's seminal 1950 essay, "Computing Machinery and Intelligence," which explored the idea of building computers capable of imitating human thought. In 1997, almost 50 years after Turing's essay, AI posted a historic breakthrough when the IBM supercomputer Deep Blue won a chess match against reigning world champion Garry Kasparov. Since then, AI's capabilities have improved rapidly, largely through advances in machine learning (ML), especially in ML models that use digital neural ...
Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce in-sample conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor.
AI and Productivity Growth: Evidence from Historical Developments in Other Technologies
An analysis of the diffusion of PCs, smart devices, cloud computing and 3D printing suggests that AI may spread in a pattern similar to those of PCs and cloud computing.