No credit history? No problem − new research suggests shopping data works as a proxy for creditworthiness

No credit history? That need not be a problem for first-time borrowing. AP Photo/Mark HumphreyIf you didn’t know much about someone, would you lend them a whole lot of money? Probably not – and banks are the same way. That’s why people with no credit history often have trouble getting loans. Banks and credit bureaus look at people’s past borrowing to predict how likely they are to repay. And when there’s no history, they tend to assume the worst.

Public debt, iMPCs & fiscal policy transmission

This paper investigates the relationship between public debt and the effectiveness of fiscal policy, presenting evidence of an inverse relationship between government debt and fiscal multipliers. To explain the results, I develop and calibrate a HANK model tailored to the U.S. economy. The model reveals that higher public debt diminishes fiscal multipliers by making households less constrained. Theoretically, I show intertemporal marginal propensities to consume (iMPCs) are sufficient statistics of public debt, influencing fiscal multipliers.

Nonlinearities and heterogeneity in firms response to aggregate fluctuations: what can we learn from machine learning?

Firms respond heterogeneously to aggregate fluctuations, yet standard linear models impose restrictive assumptions on firm sensitivities. Applying the Generalized Random Forest to U.S. firm-level data, we document strong nonlinearities in how firm characteristics shape responses to macroeconomic shocks. We show that nonlinearities significantly lower aggregate esponses, leading linear models to overestimate the economy’s sensitivity to shocks by up to 1.7 percentage points.

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