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Implement critical reforms that will produce a stronger and more resilient housing finance system.
FOSTER competitive, liquid, efficient, and resilient (CLEAR) national housing finance markets that support sustainable homeownership and affordable rental housing; OPERATE in a safe and sound manner appropriate for entities in conservatorship; and PREPARE for eventual exits from the conservatorships.
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William Larson (FHFA); Tara Sinclair (George Washington University)
Near term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. Models including Google Trends are outperformed by alternative models in nearly all periods. Our results suggest that in times of structural change there is a bias-variance tradeoff. Early on, simple approaches to exploit relevant information in the cross sectional dimension improve forecasts, but in later periods the efficiency of autoregressive models dominates.
