Beliefs are intuitive if they rely on associative memory, which can be described as a network of associations between events. A belief-theoretic characterization of the model is provided, its uniqueness properties are established, and the intersection with the Bayesian model is characterized....

We develop the first structural model of a multitasking salesforce to address questions of job design and incentive compensation design. The model incorporates three novel features: (i) multitasking effort choice given a multidimensional incentive plan; (ii) salesperson's private information...

The paper broadens the focus of empirical research on salesforce management to include multitasking settings with multidimensional incentives, where salespeople have private information about customers. This allows us to ask novel substantive questions around multidimensional incentive design...

This paper studies discounted stochastic games perfect or imperfect public monitoring and the opportunity to conduct voluntary monetary transfers. We show that for all discount factors every public perfect equilibrium payoff can be implemented with a simple class of equilibria that have a...

We provide a general framework for investigating partial identification of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures. In doing so, we derive sharp bounds for the model parameters, counterfactual behavior, and...

We introduce and solve a new class of quot;downward-recursivequot; static portfolio choice problems. An individual simultaneously chooses among ranked stochastic options, and each choice is costly. In the motivational application, just one may be exercised from those that succeed. This often...

Reduced rank regression (RRR) models with time varying heterogeneity are considered. Standard information criteria for selecting cointegrating rank are shown to be weakly consistent in semiparametric RRR models in which the errors have general nonparametric short memory components and shifting...

We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. The concept of stationarity is refined, allowing for sample size dependence in the array and paying special attention to the rate at which the boundary unit root case is...

A functional law for an I(1) sample data version of the continuous-path block bootstrap of Paparoditis and Politis (2001) is given. The results provide an alternative demonstration that continuous-path block bootstrap unit root tests are consistent under the null

This paper considers semiparametric efficient estimation of conditional moment models with possibly nonsmooth residuals in unknown parametric components (theta) and unknown functions (h) of endogenous variables. We show that: (1) the penalized sieve minimum distance (PSMD) estimator...