Working papers results

We study panel data regression models when the shocks of interest are aggregate and possibly small relative to idiosyncratic noise. This speaks to a large empirical literature that targets impulse responses via panel local projections. We show how to interpret the estimated coefficients when units have heterogeneous responses and how to obtain valid standard errors and confidence intervals. A simple recipe leads to robust inference: including lags as controls and then clustering at the time level. This strategy is valid under general error dynamics and uniformly over the degree of signal-to-noise of macro shocks.

Most societies in the world contain strong group identities and the culture supporting these groups is highly persistent. This persistence in turn gives rise to a
practical problem: how do and should societies with strong group identities organize themselves for exchange and public good provision? In this paper, we develop a theoretical framework that allows us to study, normatively and positively, the relationship between social structure, state capacity, and economic activity.

Are the players “commonly meta-certain” of an interactive belief model itself? The paper formalizes what it means by: “a player is (meta-)certain of her own belief-generating map” or “the players are (meta-)certain of the profile of belief-generating maps (i.e., the model).” The paper shows: a player is (meta-)certain of her own belief-generating map if and only if her beliefs are introspective. The players are commonly (meta-)certain of the model if and only if, for any event which some player i believes at some state, it is common belief at the state that player i believes the event. This paper then asks whether the “common meta-certainty” assumption is needed for epistemic characterizations of game-theoretic solution concepts. The paper shows: common belief in rationality leads to actions that survive iterated elimination of strictly dominated actions, as long as each player is logical and (meta-)certain only of her own strategy and belief-generating map

Algorithms are becoming the standard tool for bidding in auctions through which digital advertising is sold. To explore how algorithmic bidding might affect functioning of these auctions, this study undertakes a series of simulated experiments where bidders employ Artificial Intelligence algorithms (Q-learning and Neural Network) to bid in online advertising auctions. We consider both the generalized second-price (GSP) auction and the Vickrey-Clarke-Groves (VCG) auction. We find that the more detailed information is available to the algorithms, the better it is for the efficiency of the allocations and the advertisers profit. Conversely, the auctioneer revenues tend to decline as more complete information is available to the advertiser bidding algorithms. We also compare the outcomes of algorithmic bidding to those of equilibrium behavior in a range of different specifications and find that algorithmic bidding has a tendency to sustain low bids both under the GSP and VCG relative to competitive benchmarks. Moreover, the auctioneer revenues under the VCG setting are either close to or lower than those under the GSP setting. In addition, we consider three extensions commonly observed in the data: introduction of a non-stategic player, bidding through a common intermediary, and asymmetry of the information across bidders. Consistent with the theory, the non-strategic player presence leads to increased efficiency, whereas bidding through a common intermediary leads to lower auctioneer revenue compared to the case of individual bidding. Moreover, in experiments with information asymmetry, more informed players earn higher rewards.