Vegard Høghaug Larsen
Associate Professor of Data Science and Economics at BI Norwegian Business School
I work in the department of Data Science and Analytics at BI Norwegian Business School (BI). I am also the manager of the Simula@BI research center. I have a Master of Science in Economics from the Norwegian University of Science and Technology (NTNU), and a PhD in Economics from BI. I am a Distinguished CESifo Affiliate and also affiliated with the Centre for Applied Macroeconomics and Commodity Prices. Before my current roles, I held a position as a Senior Researcher at Norges Bank, the central bank of Norway.
My work is at the intersection of economics and data science, where I use machine learning and natural language processing techniques to study the transmission of economic shocks, understand how agents form their expectations, and develop methods to measure unobserved concepts such as sentiment, uncertainty, and climate risk. My papers have been published in journals such as Journal of Econometrics, American Economic Journal: Macroeconomics, Journal of Monetary Economics, and International Economic Review.
See my CV for more.
New publication (December 2023): Where do they care? The ECB in the media and inflation expectations with Nicolò Maffei-Faccioli and Laura Pagenhardt is out in Applied Economics Letters
This paper examines the ability of the Federal Reserve (Fed) to influence expectations of economic agents via speeches of FOMC members and regional Fed presidents. Using textual analysis, we extract an inflationary pressure index from the speeches and show that soft information about inflation impacts expectations of households, professional forecasters, and market participants. This effect is stronger after the Great Financial Crisis. We compute a measure of hawkishness of the FOMC members based on their speeches and find that professional forecasters anticipate inflation to be lower when FOMC speakers are perceived to be more willing to fight inflation. We rationalize our results through a small New Keynesian model featuring dispersed information in which the private sector solves a signal extraction problem from past and present inflation forecasts of the monetary authority.
Climate change increases the likelihood of extreme climate- and weather-related events, but also the pressure to adjust to a lower-carbon economy. We propose a measure of climate change transition risk, based on neural-network word embedding models for large-scale text analysis, and document that when it unexpectedly increases, major commodity currencies experience a persistent depreciation in line with economic theory. Expanding the analysis to a richer set of countries confirms a negative correlation between a country’s carbon export dependency and exchange rate response to transition risk. Word embeddings have been crucial for scientific advances and improvements on down-stream tasks in the Natural Language Processing literature the last decade. Our study shows how they can be used to quantify an important but hard to measure concept in economics.
Working paper available here.
Topic based uncertainty measures for Norway (daily series, updated March 2023, 14.5 MB) based on the paper Components of Uncertainty.
Norwegian Economic Policy Uncertainty (EPU) Index, (monthly series, updated March 2023, 12 KB) see the Online appendix of Components of Uncertainty for details.