Abstract: Provides estimation methods for markets in equilibrium and disequilibrium. Supports the estimation of an equilibrium and four disequilibrium models with both correlated and independent shocks. Also provides post-estimation analysis tools, such as aggregation, marginal effect, and shortage calculations. The estimation methods are based on full information maximum likelihood techniques given in Maddala and Nelson (1974) <doi:10.2307/1914215>. They are implemented using the analytic derivative expressions calculated in Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>. Standard errors can be estimated by adjusting for heteroscedasticity or clustering. The equilibrium estimation constitutes a case of a system of linear, simultaneous equations. The disequilibrium models, instead, replace the market-clearing condition with a non-linear, short-side rule and allow for different specifications of price dynamics. Keywords: disequilibrium, full-information-maximum-likelihood, market-clearing
Abstract: The code implements a simple version of the radial attention model and solves it. The solver is based on a concurrent re-formulation of the value function iteration algorithm. Further, it uses an adaptive search grid implementation to provide more accurate optimal control approximations. For more implementation details see the documentation. Keywords: attentional costs, endogenous choice sets, learning, radial attention, value function iteration