We study the economics and finance scholars' reaction to the 2008 financial crisis using machine learning language analyses methods of Latent Dirichlet Allocation and dynamic topic modelling algorithms, to analyze the texts of 14,270 NBER working papers covering the 1999–2016 period. We find...

We study the economics and finance scholars' reaction to the 2008 financial crisis using machine learning language analyses methods of Latent Dirichlet Allocation and dynamic topic modelling algorithms, to analyze the texts of 14,270 NBER working papers covering the 1999-2016 period. We find...

Innovations contribute to economic growth. Hence, knowledge about drivers of innovation activities is a necessary input for economic policy making when it comes to implement targeted support measures. We focus on firms as potential drivers of innovation and use a novel data-driven approach to...

The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as "data" in a downstream econometric model. We establish theoretical arguments for why...

The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as "data" in a downstream econometric model. We establish theoretical arguments for why...

I utilize latent Dirichlet allocation and dynamic topic model that are machine learning algorithms across a data set encompassing 25,990 financial accounting articles issued from 1956 to 2023 in 16 accounting journals, and impartially ascertain 20 research topics. The topics of mergers and...