We build a life-cycle model with earnings risk, liquidity constraints, and portfolio choice over tax-deferred and taxable assets to evaluate how household consumption changes in response to shocks to transitory anticipated income, such as the 2001 income tax rebate. Households optimally invest in tax-deferred assets, which are encumbered by withdrawal penalties, and exchange taxable precautionary savings for higher after-tax returns. The model predicts a higher marginal propensity to consume out of a rebate than is predicted by a standard frictionless life-cycle model. Liquidity-constrained households-with few financial assets or portfolios expensive to reallocate-consume a higher fraction of the rebates. (JEL D91, E21, G11, H24)

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We exploit a novel dataset on mortgages offered by banks through Italy's main online mortgage broker, which works with banks representing over 80 per cent of mortgages granted, to gain an up-to-date assessment of loan supply conditions. Characteristics of mortgages are reported for about 85,000 borrower-contract profiles, constant over time, available at the beginning of each month starting from March 2018. We document that riskier applications, characterized by high loan-to-value ratios, low borrower's income and long maturity, are, on average, offered by a smaller number of banks that charge higher interest rates. Online banks tend to provide better price conditions than traditional intermediaries. We use the online rates offered to nowcast bank-level official (MIR) interest rate statistics, available only several weeks later. By relying on both regression analyses and machine learning algorithms, we show that the rates offered have significant predictive content for fixed-rate contracts, also after controlling for time-varying demand conditions, market reference rates, and unobserved time-invariant bank characteristics. Machine learning algorithms provide further improvements over regression models in out of sample predictions.