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Expecting the unexpected: Stressed scenarios for economic growth (Replication...
We propose the construction of conditional growth densities under stressed factor scenarios to assess the level of exposure of an economy to small probability but potentially... -
The shale oil boom and the US economy: Spillovers and time-varying effects (r...
Hilde C. Bjørnland & Julia Skretting, "The Shale Oil Boom and the U.S. Economy: Spillovers and Time-Varying Effects", Journal of Applied Econometrics The data used in this... -
The benefits of forecasting inflation with machine learning: New evidence (re...
This is the replication package for "The benefits of forecasting inflation with machine learning: New evidence" by A. Naghi, E. O'Neill, and M. Zaharieva, Journal of Applied... -
Addressing sample selection bias for machine learning methods (replication data)
Addressing sample selection bias for machine learning methods (replication data) Dylan Brewer and Alyssa Carlson Accepted at Journal of Applied Econometrics, 2023 Overview This... -
Harmonization of product classifications: A consistent time series of economi...
Firm-product data provide information for various research questions in international trade or innovation economics. However, working with these data require harmonizing product... -
Exchange rates and macroeconomic fundamentals (replication data)
We examine the relationship between exchange rates and macroeconomic fundamentals using a two-step maximum likelihood estimator through which we compute time-varying factor... -
US Weekly Economic Index: Replication and extension (replication data)
Replication materials for "US Weekly Economic Index: Replication and extension", by Philipp Wegmueller and Christian Glocker, Journal of Applied Econometrics, forthcoming. -
Quantifying investor narratives and their role during COVID-19 (replication f...
This paper elicits and quantifies narratives from open-ended surveys sent daily to U.S. stockholders during the first wave of the COVID-19 pandemic. Using textual analysis, we...
