21 research outputs found
Identification of semiparametric discrete outcome models with bounded covariates
Identification of discrete outcome models is often established by using
special covariates that have full support. This paper shows how these
identification results can be extended to a large class of commonly used
semiparametric discrete outcome models when all covariates are bounded. I apply
the proposed methodology to multinomial choice models, bundles models, and
finite games of complete information
2022-5 Estimation of Parametric Binary Outcome Models with Degenerate Pure Choice-Based Data with Application to COVID-19-Positive Tests from British Columbia
I propose a generalized method of moments type procedure to estimate parametric binary choice models when the researcher only observes degenerate pure choices-based or presence-only data and has some information about the distribution of the covariates. This auxiliary information comes in the form of moments. I present an application based on the data on all COVID-19-positive tests from British Columbia. Publicly available demographic information on the population in British Columbia allows me to estimate the conditional probability of a person being COVID-19-positively tested conditional on demographic
2022-4 Identification and Estimation of Multinomial Choice Models with Latent Special Covariates
Identification of multinomial choice models is often established by using special covariates that have full support. This paper shows how these identification results can be extended to a large class of multinomial choice models when all covariates are bounded. I also provide a new √n-consistent asymptotically normal estimator of the finite-dimensional parameters of the model
Prices, Profits, Proxies, and Production
This paper studies nonparametric identification and counterfactual bounds for
heterogeneous firms that can be ranked in terms of productivity. Our approach
works when quantities and prices are latent rendering standard approaches
inapplicable. Instead, we require observation of profits or other
optimizing-values such as costs or revenues, and either prices or price proxies
of flexibly chosen variables. We extend classical duality results for
price-taking firms to a setup with discrete heterogeneity, endogeneity, and
limited variation in possibly latent prices. Finally, we show that convergence
results for nonparametric estimators may be directly converted to convergence
results for production sets.Comment: This paper was previously circulated with the title "Prices, Profits,
and Production
2019-3 Discerning Solution Concepts
The empirical analysis of discrete complete-information games has relied on
behavioral restrictions in the form of solution concepts, such as Nash
equilibrium. Choosing the right solution concept is crucial not just for
identification of payoff parameters, but also for the validity and
informativeness of counterfactual exercises and policy implications. We say
that a solution concept is discernible if it is possible to determine whether
it generated the observed data on the players' behavior and covariates. We
propose a set of conditions that make it possible to discern solution concepts.
In particular, our conditions are sufficient to tell whether the players'
choices emerged from Nash equilibria. We can also discern between
rationalizable behavior, maxmin behavior, and collusive behavior. Finally, we
identify the correlation structure of unobserved shocks in our model using a
novel approach
2022-3 A Random Attention and Utility Model
We generalize the stochastic revealed preference methodology of McFadden and Richter (1990) for finite choice sets to settings with limited consideration. Our approach is nonparametric and requires partial choice set variation. We impose a monotonicity condition on attention first proposed by Cattaneo et al. (2020) and a stability condition on the marginal distribution of preferences. Our framework is amenable to statistical testing. These new restrictions extend widely known parametric models of consideration with heterogeneous preferences
Peer Effects in Consideration and Preferences
We develop a general model of discrete choice that incorporates peer effects
in preferences and consideration sets. We characterize the equilibrium behavior
and establish conditions under which all parts of the model can be recovered
from a sequence of choices. We allow peers to affect only preferences, only
consideration, or both. We exploit different types of variations to separate
the peer effects in preferences and consideration sets. This allows us to
recover the set (and type) of connections between the agents in the network. We
then use this information to recover the random preferences and the attention
mechanisms of each agent. These nonparametric identification results allow
unrestricted heterogeneity across agents and do not rely on the variation of
either covariates or the set of available options (or menus). We apply our
results to model expansion decisions by coffee chains and find evidence of
limited consideration. We simulate counterfactual predictions and show how
limited consideration slows down competition
2023-2 Dynamic and Stochastic Rational Behavior
We analyze consumer demand behavior using Dynamic Random Utility Model (DRUM). Under DRUM, a consumer draws a utility function from a stochastic utility process in each period and maximizes this utility subject to her budget constraint. DRUM allows unrestricted time correlation and cross-section heterogeneity in preferences. We fully characterize DRUM for a panel data of consumer choices and budgets. DRUM is linked to a finite mixture of deterministic behavior represented as the Kronecker product of static rationalizable behavior. We provide a generalization of the Weyl-Minkowski theorem that uses this link and enables conversion of the characterizations of the static Random Utility Model (RUM) of McFadden-Richter (1990) to its dynamic form. DRUM is more flexible than Afriat’s (1967) framework for time series and more informative than RUM. We show the feasibility of the statistical test of DRUM in a Monte Carlo study