We study identification and inference in first-price auctions with risk averse bidders and selective entry, building on a flexible entry and bidding framework we call the Affiliated Signal with Risk Aversion (AS-RA) model. Assuming that the econometrician observes either exogenous variation in the number of potential bidders (N) or a continuous instrument (z) shifting opportunity costs of entry, we provide a sharp characterization of the nonparametric restrictions implied by equilibrium bidding. Given variation in either competition or costs, this characterization implies that risk neutrality is nonparametrically testable in the sense that if bidders are strictly risk averse, then no risk neutral model can rationalize the data. In addition, if both instruments (discrete N and continuous z) are available, then the model primitives are nonparametrically point identified. We then explore inference based on these identification results, focusing on set inference and testing when primitives are set identified. Keywords: Auctions, entry, risk aversion, identification, set inference.
This paper lists 19 points that follow from results I have obtained using a structural macro-economic model (SEM). Such models are more closely tied to the aggregate data than are DSGE models, and I argue that DSGE models and similar models should have properties that are consistent with these points. The aim is to try to bring macro back to its empirical roots.
The propensity of consumers to engage in word-of-mouth (WOM) can differ after good versus bad experiences. This can result in positive or negative selection of user-generated reviews. We show how the strength of brand image - determined by the dispersion of consumer beliefs about quality - and the informativeness of good and bad experiences impact the selection of WOM in equilibrium. Our premise is that WOM is costly: Early adopters talk only if their information is instrumental for the receiver’s purchase decision. If the brand image is strong, i.e., consumers have close to homogeneous beliefs about quality, then only negative WOM can arise. With a weak brand image, positive WOM can occur if positive experiences are sufficiently informative. We show that our theoretical predictions are consistent with restaurant review data from Yelp.com. A review rating for a national established chain restaurant is almost 1-star lower (on a 5-star scale) than a review rating for a comparable independent restaurant, controlling for various reviewer and restaurant characteristics. Further, negative chain restaurant reviews have more instances of expectation words, indicating agreement over beliefs about the quality, whereas positive reviews of independent restaurants feature disproportionately many novelty words.
The propensity of consumers to engage in word-of-mouth (WOM) can differ after good versus bad experiences, resulting in positive or negative selection of user-generated reviews. We study how the propensity to engage in WOM depends on information available to customers through different marketing channels. We develop a model of WOM in which a target customer makes a purchase decision based on his private brand association, public product-specific information (e.g. from advertising or past reviews) and WOM content, and an early adopter of the new product engages in WOM only if her information is instrumental to the target customer’s purchase decision. We define brand image to be the distribution of the customers’ brand associations, and strength of the brand image to be the precision of this distribution. We show that if the brand image is strong, then in equilibrium only negative WOM can arise. In contrast, with a weak brand image, positive WOM must occur. Moreover, holding product quality fixed, a positive advertising signal realization leads to a more positive WOM selection. We use restaurant review data from Yelp.com to motivate our model assumptions and validate the predictions. For example, a textual analysis of reviews is consistent with prevalence of an instrumental motive for WOM. Further, a review rating for national established chain restaurant locations, where the brand image is strong, is almost 1-star lower (on a 5-star scale) than a review rating for a comparable independent restaurant, controlling for reviewer and restaurant characteristics.
Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial or network interaction. SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. The choice of a SAR model over other alternatives, such as spatial Durbin (SD) or spatial lagged X (SLX) models, is often arbitrary, raising issues of potential specification error. To address such issues, this paper develops an omnibus specification test within the SAR framework that can detect general forms of misspecification including that of the spatial weight matrix, functional form and the model itself. The approach extends the framework of conditional moment testing of Bierens (1982, 1990) to the general spatial setting. We derive the asymptotic distribution of our test statistic under the null hypothesis of correct SAR specification and show consistency of the test. A Monte Carlo study is conducted to study finite sample performance of the test. An empirical illustration on the performance of our test in the modelling of tax competition in Finland and Switzerland is included.
I consider a bargaining game with two types of players – rational and stubborn. Rational players choose demands at each point in time. Stubborn players are restricted to choose from the set of “insistent” strategies that always make the same demand and never accept anything less. However, their initial choice of demand is unrestricted. I characterize the equilibria of this game. I show that while pooling equilibria exist, fully separating equilibria do not. Relative to the case with exogenous behavioral types, strong behavioral predictions emerge: in the limit, players randomize over at most two demands. However, unlike in a world with exogenous types, there is Folk-theorem-like payoff multiplicity.
The propensity of consumers to engage in word-of-mouth (WOM) differs after good versus bad experiences, which can result in positive or negative selection of user-generated reviews. We show how the dispersion of consumer beliefs about quality (brand strength), informativeness of good and bad experiences, and price can affect selection of WOM in equilibrium. WOM is costly: Early adopters talk only if they can affect the receiver’s purchase. Under homogeneous beliefs, only negative WOM can arise. Under heterogeneous beliefs, the type of WOM depends on the informativeness of the experiences. We use data from Yelp.com to validate our predictions.