Previous solution methods could not handle large-scale OLG models such as ours due to the well-known curse of dimensionality. The prior state of the art uses sparse-grid methods to handle 10 to 30 periods depending on the model's realism. Other methods used to solve large-scale, multi- period...

We apply unique loan level data from New Century Financial Corporation, a major subprime lender, to assess whether attributes of Congressional Representatives were associated with access to and pricing of subprime mortgage credit. Research findings indicate higher likelihoods of subprime loan...

We study the interplay of optimal ex-ante (macroprudential) and ex-post (monetary or fiscal stimulus) measures to respond to systemic financial crises in a tractable model of fire sales. We find that it is generally optimal to use both, rejecting the Greenspan doctrine to only intervene ex post....

We study the impact of two dimensions of trust, namely trust in business elites and trust in government, on policy preferences. Using a randomized online survey, we find that our two treatments are effective in changing trust in Major Companies and in Courts/Government. In contrast to previous...

Governments play a central role in facilitating economic development. Yet while economists have long emphasized the importance of government quality, historically they have paid less attention to the internal workings of the state and the individuals who provide the public services. This paper...

The paper presents a dynamic portfolio model under currency inconvertibility which rationalizes the recent Egyptian experience of real exchange rate appreciation and currency diversification following the increase in oil exports and the partial financial liberalization that took place after...

We experimentally study the impact of substantially larger enterprise loans, in collaboration with an Egyptian lender. Larger loans generate small average impacts, but machine learning using psychometric data reveals dramatic heterogeneity. Top-performers (i.e., those with the highest predicted...