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Discussion Paper
Using Generative AI Models to Understand FOMC Monetary Policy Discussions
In an era increasingly shaped by artificial intelligence (AI), the public’s understanding of economic policy may be filtered through the lens of generative AI models (also called large language models or LLMs). Generative AI models offer the promise of quickly ingesting and interpreting large amounts of textual information.
Discussion Paper
Which Market Indicators Best Forecast Recessions?
In this note, we use econometric methods to infer which economic and financial indicators reliably identify and predict recessions.
Working Paper
What's the Story? A New Perspective on the Value of Economic Forecasts
We apply textual analysis tools to measure the degree of optimism versus pessimism of the text that describes Federal Reserve Board forecasts published in the Greenbook. The resulting measure of Greenbook text sentiment, ?Tonality,? is found to be strongly correlated, in the intuitive direction, with the Greenbook point forecast for key economic variables such as unemployment and inflation. We then examine whether Tonality has incremental power for predicting unemployment, GDP growth, and inflation up to four quarters ahead. We find it to have significant and substantive predictive power for ...
Working Paper
Evaluating the Conditionality of Judgmental Forecasts
We propose a framework to evaluate the conditionality of forecasts. The crux of our framework is the observation that a forecast is conditional if revisions to the conditioning factor are faithfully incorporated into the remainder of the forecast. We consider whether the Greenbook, Blue Chip, and the Survey of Professional Forecasters exhibit systematic biases in the manner in which they incorporate interest rate projections into the forecasts of other macroeconomic variables. We do not find strong evidence of systematic biases in the three economic forecasts that we consider, as the interest ...