Publications

To explore how speculative trading influences prices in financial markets, we conduct a laboratory market experiment with speculating investors (who do not collect dividends and trade only for capital gains) and dividend-collecting investors. Moreover, we operate markets at two different levels of money supply. We find that in phases with only speculating investors present (i) price deviations from fundamentals are larger; (ii) prices are more volatile; (iii) mispricing increases with the number of transfers until maturity; and (iv) speculative trading pushes prices upward (downward) when the supply of money is high (low). These results suggest that controlling the money supply can help to stabilize asset prices.

The Yale Labor Survey (YLS) uses online panels to estimate the state of the US labor market in real time. It is designed to parallel the US government’s monthly labor force survey and present weekly information rapidly and inexpensively. Using an experimental design, the YLS estimates that the US unemployment rate peaked in late April and improved substantially by mid-June. The YLS unemployment rate in mid-June is estimated to be 15%, down about 2 percentage points from mid-May.

Common resources may be managed with inefficient policies for the sake of equity. We study how rationing the commons shapes the efficiency and equity of resource use, in the context of agricultural groundwater use in Rajasthan, India. We find that rationing binds on input use, such that farmers, despite trivial prices for water extraction, use roughly the socially optimal amount of water on average. The rationing regime is still grossly inefficient, because it misallocates water across farmers, lowering productivity. Pigouvian reform would increase agricultural surplus by 12% of household income, yet fall well short of a Pareto improvement over rationing.

This paper proposes simple, data-driven, optimal rate-adaptive inferences on a structural function in semi-nonparametric conditional moment restrictions. We consider two types of hypothesis tests based on leave-one-out sieve estimators. A structure- space test (ST) uses a quadratic distance between the structural functions of endogenous variables; while an image-space test (IT) uses a quadratic distance of the conditional moment from zero. For both tests, we analyze their respective classes of nonparametric alternative models that are separated from the null hypothesis by the minimax rate of testing. That is, the sum of the type I and the type II errors of the test, uniformly over the class of nonparametric alternative models, cannot be improved by any other test. Our new minimax rate of ST differs from the known minimax rate of estimation in nonparametric instrumental variables (NPIV) models. We propose computationally simple and novel exponential scan data-driven choices of sieve regularization parameters and adjusted chi-squared critical values. The resulting tests attain the minimax rate of testing, and hence optimally adapt to the unknown smoothness of functions and are robust to the unknown degree of ill-posedness (endogeneity). Data-driven confidence sets are easily obtained by inverting the adaptive ST. Monte Carlo studies demonstrate that our adaptive ST has good size and power properties in finite samples for testing monotonicity or equality restrictions in NPIV models. Empirical applications to nonparametric multi-product demands with endogenous prices are presented.

We propose a new adaptive hypothesis test for polyhedral cone (e.g., monotonicity, convexity) and equality (e.g., parametric, semiparametric) restrictions on a structural function in a nonparametric instrumental variables (NPIV) model. Our test statistic is based on a modified leave-one-out sample analog of a quadratic distance between the restricted and unrestricted sieve NPIV estimators. We provide computationally simple, data-driven choices of sieve tuning parameters and adjusted chi-squared critical values. Our test adapts to the unknown smoothness of alternative functions in the presence of unknown degree of endogeneity and unknown strength of the instruments. It attains the adaptive minimax rate of testing in L2. That is, the sum of its type I error uniformly over the composite null and its type II error uniformly over nonparametric alternative models cannot be improved by any other hypothesis test for NPIV models of unknown regularities. Data-driven confidence sets in L2 are obtained by inverting the adaptive test. Simulations con rm that our adaptive test controls size and its nite-sample power greatly exceeds existing non-adaptive tests for monotonicity and parametric restrictions in NPIV models. Empirical applications to test for shape restrictions of differentiated products demand and of Engel curves are presented.

The coronavirus (COVID-19) pandemic has halted economic activity worldwide, hurting firms and pushing them toward bankruptcy. This paper provides a unified framework to organize the policy debate related to firm financing during the downturn, centered along four main points. First, the economic crisis triggered by the spread of the virus is radically different from past crises, with important consequences for optimal policy responses. Second, to avoid inefficient bankruptcies and long-term detrimental effects, it is important to preserve firms’ relationships with key stakeholders, like workers, suppliers, customers, and creditors. Third, firms can benefit from “hibernating,” using the minimum bare cash necessary to withstand the pandemic, while using credit to remain alive until the crisis subdues. Fourth, the existing legal and regulatory infrastructure is ill-equipped to deal with an exogenous systemic shock such as this pandemic. Financial sector policies can help increase the provision of credit, while posing difficult choices and trade-offs.

We analyze lifecycle saving strategies using a recursive utility model calibrated to match empirical estimates of the value of a statistical life. The novelty of our approach is that we require preferences to be monotone with respect to first order stochastic dominance. The framework we use can disentangle risk aversion and the intertemporal elasticity and can feature a positive value of life without placing constraints on the value of the risk aversion parameter or the intertemporal elasticity of substitution. We show that, with a positive value of life, risk aversion reduces savings, decreases stock market participation and decreases annuity purchase. Risk averse agents are willing to make an early death a not-so-adverse outcome by enjoying greater consumption when young and bequeathing wealth in case of death. The model can rationalize low annuity demand while also matching empirically documented levels of wealth and private investments in stocks.