This paper provides evidence that the U.S. dollar affects trade through a financial channel of the exchange rate. Using global data over three decades, we show that dollar appreciation increases import prices and decreases import quantities for non-U.S. dollar countries. In line with a financial channel, these effects are stronger when the exporting country borrows more in U.S. dollars abroad. The financial channel was active before the global financial crisis, has strengthened since, and operates independently of the dominant currency invoicing channel. Instrumenting the dollar is key to uncovering the full effect of the financial channel.

This paper combines a data rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors ("belief distortions") embedded in survey responses. We find that distortions are large even for professional forecasters, with all respondent-types over-weighting their own beliefs relative to publicly available information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with biases in expectations evolving dynamically in response to cyclical shocks. The results suggest that artificial intelligence algorithms can be productively deployed to correct errors in human judgement and improve predictive accuracy.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.