This paper proposes a new double-question survey whereby an individual is presented with two sets of questions; one on beliefs about current asset values and another on price expectations. A theoretical asset pricing model with heterogeneous agents is advanced and the existence of a negative relationship between price expectations and asset valuations is established, and is then tested using survey results on equity, gold and house prices. Leading indicators of bubbles and crashes are proposed and their potential value is illustrated in the context of a dynamic panel regression of realized house price changes across key Metropolitan Statistical Areas (MSAs) in the US. In an out-of-sample forecasting exercise it is also shown that forecasts of house price changes (pooled across MSAs) that make use of bubble and crash indicators perform significantly better than a benchmark model that only uses lagged and expected house price changes.
Keywords: Price expectations, bubbles and crashes, house prices, belief valuations.
Spatial Equilibrium and Search Frictions - an Application to the NYC Taxi Market
Downloads: working paper
This paper uses a dynamic spatial equilibrium model to analyze the effect of matching frictions and pricing policy on the spatial allocation of taxicabs and the aggregate number of taxi-passenger meetings. A spatial equilibrium model, in which meetings are frictionless but aggregate matching frictions can arise endogenously for certain parameter values, is calibrated using data on more than 45 million taxi rides in New York. It is shown how the set of equilibria changes for different pricing rules and different levels of aggregate market tightness, defined as the ratio of total supply to total demand. Finally, a novel data-driven algorithm for inferring unobserved demand from the data is proposed, and is applied to analyze how the relationship between demand and supply in a market with frictions compares to the frictionless equilibrium outcome.
Keywords: spatial equilibrium, matching, industry dynamics, taxicabs
Estimation Of Peer Effects In Endogenous Social Networks: Control Function Approach
We propose a method of estimating the linear-in-means model of peer effects in which the peer group, defined by a social network, is endogenous in the outcome equation for peer effects. Endogeneity is due to unobservable individual characteristics that influence both link formation in the network and the outcome of interest. We propose two estimators of the peer effect equation that control for the endogeneity of the social connections using a control function approach. We leave the functional form of the control function unspecified and treat it as unknown. To estimate the model, we use a sieve semiparametric approach, and we establish asymptotics of the semiparametric estimator.
Keywords: peer effects, endogenous network, sieve estimation, control function
A Bayesian Comparison of Models of Network Formation
Downloads: working paper
A prominent feature of real-world social networks is a high level of clustering. I review different approaches to modeling network formation and clustering and I apply Bayesian model selection to evaluate the models. Preliminary results confirm that models that treat links as pairwise independent do not generate the levels of clustering observed in the data. Models that include unobserved heterogeneity perform slightly better than models with only observable characteristics.
Keywords: network formation, Bayesian model selection