June 2019

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Friday, June 14, 2019 — 10:30 to 10:30 AM EDT

Aggregate Risk and Bank Regulation in General Equilibrium

We examine the optimal design of bank regulation in a general equilibrium model. The unregulated economy has multiple equilibria that feature varying sizes of the financial sector and bank fragility. The economy underinvests (overinvests) in risky production when aggregate risk is low (high). We characterize and implement the efficient allocations via capital and reserve requirements, deposit insurance and bailouts. There is a range of efficient regulatory policies with a stricter capital requirement on banks being accompanied by a looser reserve requirement and less deposit insurance. We derive novel insights into how aggregate risk influences capital and reserve requirements as well as the efficiency of depositor subsidies.

Monday, June 24, 2019 — 4:00 PM EDT

A regularization approach to the dynamic panel data model estimation

In a dynamic panel data model, the number of moment conditions may be very large even if the time dimension is moderately large. Even though the use of many moment conditions improves the asymptotic efficiency, the inclusion of an excessive number of moment conditions increases the bias in finite samples. An immediate consequence of a large number of instruments is a large dimensional covariance matrix of the instruments. As a consequence, the condition number (the largest eingenvalue divided by the smallest one) is very high especially when the autoregressive parameter is close to unity. Inverting covariance matrix of instruments with high condition number can badly impact the properties of the estimators. This paper proposes a regularization approach to the estimation of such models using three regularization schemes based on three different ways of inverting the covariance matrix of the instruments. Under double asymptotic, we show that our regularized estimators are consistent and asymptotically normal. These regularization schemes involve a regularization or smoothing parameter so that we derive a data driven selection of this regularization parameter based on an approximation of the Mean Square Error and show its optimality. The simulations confirm that regularization improves the properties of the usual GMM estimator. As empirical application, we investigate the effect of financial development on economic growth. Regularization corrects the bias of the usual GMM estimator which seems to underestimate the financial development - economic growth effect.

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