The most recent recession has been associated with a financial crisis that led to a large widening of spreads and quantitative restrictions on lending. As well as affecting investment, such a credit contraction is likely to have had a large effect on the working capital positions of UK firms and...

Most macroeconomic forecasters underestimated global investment during the late 1990s. One potential reason was that the models they were using were insufficiently disaggregated. In this paper, an empirical model is estimated whose out-of-sample forecasts largely predicted the global investment...

We test whether quantitative easing (QE), in addition to boosting aggregate demand and inflation via portfolio rebalancing channels, operated through a bank lending channel (BLC) in the UK. Using Bank of England data together with an instrumental variables approach, we find no evidence of a...

We test whether quantitative easing (QE) provided a boost to bank lending in the United Kingdom, in addition to the effects on asset prices, demand and inflation focused on in most other studies. Using a data set available to researchers at the Bank, we use two alternative approaches to identify...

The pandemic catalyzed an enduring shift to remote work. To measure and characterize this shift, we examine more than 250 million job vacancy postings across five English-speaking countries. Our measurements rely on a state-of-the-art languageprocessing framework that we fit, test, and refine...

A frequent challenge when using graphical models in applications is that the sample size is limited relative to the number of parameters to be learned. Our motivation stems from applications where one has external data, in the form of networks between variables, that provides valuable...

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...

Firm-level stock returns differ enormously in reaction to COVID-19 news. We characterize these reactions using the Risk Factors discussions in pre-pandemic 10-K filings and two text-analytic approaches: expert-curated dictionaries and supervised machine learning (ML). Bad COVID-19 news lowers...

We use a unique corpus of job descriptions for C-suite positions to document skills requirements in top managerial occupations across a large sample of firms. A novel algorithm maps the text of each executive search into six separate skill clusters reflecting cognitive, interpersonal, and...