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Journal Article
Assessing the Risk of Extreme Unemployment Outcomes
Although the unemployment rate is at a historically low level, many policymakers are nevertheless watching projections for the future unemployment rate closely to evaluate the risk of extreme outcomes. We assess the probabilities of extreme outcomes in the near and medium term and find that the risk of unexpectedly high unemployment three years in the future has declined from its Great Recession peak and remained low over the past three years.
Journal Article
Testing Hybrid Forecasts for Imports and Exports
The quality of economic forecasts tends to deteriorate during times of stress such as the COVID-19 pandemic, raising questions about how to improve forecasts during exceptional times. One method of forecasting that has received less attention is refining model-based forecasts with judgmental adjustment, or hybrid forecasting. Judgmental adjustment is the process of incorporating information from outside a model into a forecast or adjusting a forecast subjectively. Hybrid forecasts could be particularly useful during extraordinary times such as the COVID-19 pandemic, as models that do not ...
Working Paper
Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values
Machine learning and artificial intelligence methods are often referred to as “black boxes” when compared with traditional regression-based approaches. However, both traditional and machine learning methods are concerned with modeling the joint distribution between endogenous (target) and exogenous (input) variables. Where linear models describe the fitted relationship between the target and input variables via the slope of that relationship (coefficient estimates), the same fitted relationship can be described rigorously for any machine learning model by first-differencing the partial ...
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, ...
Journal Article
China's Post-COVID Recovery: Implications and Risks
China removed most of its COVID-19 restrictions in November 2022 following a year of weak growth. Despite initial uncertainty about sustained COVID-19 outbreaks, the Chinese economy has begun to rebound, driven by domestic consumption. The rebound is likely to boost global growth.
Journal Article
How Much Would China’s GDP Respond to a Slowdown in Housing Activity?
We analyze China's interindustry connections and show that China?s housing activity has become increasingly important to its GDP growth. Our results suggest that a 10 percent decline in final demand for real estate and housing-related construction would lead to a decline in total output of 2.2 percent, an effect more than two times larger than it would have been 10 years ago.
Journal Article
Revamping the Kansas City Financial Stress Index Using the Treasury Repo Rate
The Kansas City Financial Stress Index (KCFSI) uses the London Interbank Offered Rate (LIBOR) to measure money market borrowing conditions. But regulatory changes in the United Kingdom will eliminate LIBOR by 2021. We construct a revised financial stress index with a variable that measures the cost of borrowing collateralized by Treasury securities (the Treasury repo rate) instead of LIBOR. {{p}} This revised measure of the KCFSI is highly correlated with the current KCFSI, suggesting the Treasury repo rate can replace LIBOR.
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
Assessing Macroeconomic Tail Risks in a Data-Rich Environment
We use a large set of economic and financial indicators to assess tail risks of the three macroeconomic variables: real GDP, unemployment, and inflation. When applied to U.S. data, we find evidence that a dense model using principal components (PC) as predictors might be misspecified by imposing the “common slope” assumption on the set of predictors across multiple quantiles. The common slope assumption ignores the heterogeneous informativeness of individual predictors on different quantiles. However, the parsimony of the PC-based approach improves the accuracy of out-of-sample forecasts ...