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Showing results 1 to 10 of approximately 25.
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Working Paper
Machine Learning, the Treasury Yield Curve and Recession Forecasting
We use machine learning methods to examine the power of Treasury term spreads and other financial market and macroeconomic variables to forecast US recessions, vis-à-vis probit regression. In particular we propose a novel strategy for conducting cross-validation on classifiers trained with macro/financial panel data of low frequency and compare the results to those obtained from standard k-folds cross-validation. Consistent with the existing literature we find that, in the time series setting, forecast accuracy estimates derived from k-folds are biased optimistically, and cross-validation ...
Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce in-sample conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor.
Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor. We argue that this method of generating forecasts is inexpensive and can be ...
Working Paper
Understanding Models and Model Bias with Gaussian Processes
Despite growing interest in the use of complex models, such as machine learning (ML) models, for credit underwriting, ML models are difficult to interpret, and it is possible for them to learn relationships that yield de facto discrimination. How can we understand the behavior and potential biases of these models, especially if our access to the underlying model is limited? We argue that counterfactual reasoning is ideal for interpreting model behavior, and that Gaussian processes (GP) can provide approximate counterfactual reasoning while also incorporating uncertainty in the underlying ...
Working Paper
The Anatomy of Out-of-Sample Forecasting Accuracy
We introduce the performance-based Shapley value (PBSV) to measure the contributions of individual predictors to the out-of-sample loss for time-series forecasting models. Our new metric allows a researcher to anatomize out-of-sample forecasting accuracy, thereby providing valuable information for interpreting time-series forecasting models. The PBSV is model agnostic—so it can be applied to any forecasting model, including "black box" models in machine learning, and it can be used for any loss function. We also develop the TS-Shapley-VI, a version of the conventional Shapley value that ...
Working Paper
Reasons Behind Words: OPEC Narratives and the Oil Market
We analyze the content of the Organization of the Petroleum Exporting Countries (OPEC) communications and whether it provides information to the crude oil market. To this end, we derive an empirical strategy which allows us to measure OPEC's public signal and test whether market participants find it credible. Using Structural Topic Models, we analyze OPEC narratives and identify several topics related to fundamental factors, such as demand, supply, and speculative activity in the crude oil market. Importantly, we find that OPEC communication reduces oil price volatility and prompts market ...
Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce in-sample conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor.
Working Paper
Macroeconomic Indicator Forecasting with Deep Neural Networks
Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models {{p}} that exhibit model dependence and have high data demands. {{p}} We explore deep neural networks as an {{p}} opportunity to improve upon forecast accuracy with limited data and while remaining agnostic as to {{p}} functional form. We focus on predicting civilian unemployment using models based on four different neural network architectures. Each of these models outperforms bench- mark models at short time horizons. One model, ...
Working Paper
Reasons Behind Words: OPEC Narratives and the Oil Market
We analyze the content of the Organization of the Petroleum Exporting Countries (OPEC) communications and whether it provides information to the crude oil market. To this end, we derive an empirical strategy which allows us to measure OPEC's public signal and test whether market participants find it credible. Using Structural Topic Models, we analyze OPEC narratives and identify several topics related to fundamental factors, such as demand, supply, and speculative activity in the crude oil market. Importantly, we find that OPEC communication reduces oil price volatility and prompts market ...
Working Paper
Reasons Behind Words: OPEC Narratives and the Oil Market
We analyze the content of the Organization of the Petroleum Exporting Countries (OPEC) communications and whether it provides information to the crude oil market. To this end, we derive an empirical strategy which allows us to measure OPEC's public signal and test whether market participants find it credible. Using Structural Topic Models, we analyze OPEC narratives and identify several topics related to fundamental factors, such as demand, supply, and speculative activity in the crude oil market. Importantly, we find that OPEC communication reduces oil price volatility and prompts market ...