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Uncertainty Shocks in a Model of Effective Demand: Comment
Basu and Bundick (2017) show a second moment intertemporal preference shock creates meaningful declines in output in a sticky price model with Epstein and Zin (1991) preferences. The result, however, rests on the way they model the shock. If a preference shock is included in Epstein-Zin preferences, the distributional weights on current and future utility must sum to 1, otherwise it creates an asymptote in the response to the shock with unit intertemporal elasticity of substitution. When we change the preferences so the weights sum to 1, the asymptote disappears as well as their main ...
The Impact of Health and Economic Policies on the Spread of COVID-19 and Economic Activity
This paper empirically investigates the causal linkages between COVID-19 spread, government health containment and economic support policies, and economic activity during 2020 in the U.S. We model their joint dynamics as generated by a structural vector autoregression and estimate it using U.S. state-level data. We identify structural shocks to the variables by making assumptions on their short-run relation consistent with salient epidemiological and economic features of COVID-19. We isolate the direct impact of COVID-19 spread and policy responses on economic activity by controlling for ...
Lingering Residual Seasonality in GDP Growth
Measuring economic growth is complicated by seasonality, the regular fluctuation in economic activity that depends on the season of the year. The Bureau of Economic Analysis uses statistical techniques to remove seasonality from its estimates of GDP, and, in 2015, it took steps to improve the seasonal adjustment of data back to 2012. I show that residual seasonality in GDP growth remains even after these adjustments, has been a longer-term phenomenon, and is particularly noticeable in the 1990s. The size of this residual seasonality is economically meaningful and has the ability to change the ...
Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning
This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample predicted default probabilities to construct a leading indicator of non-financial corporate health. The index predicts real economic outcomes such as GDP growth and employment up to eight quarters ahead. Impulse responses validate the interpretation of the index as a measure of financial stress.