My research focuses on rational inattention and applied time series econometrics.
Mechanics of static quadratic Gaussian rational inattention tracking problems
This paper presents a general framework for constructing and solving the multivariate static linear quadratic Gaussian (LQG) rational inattention tracking problem. We interpret the nature of the solution and the implied action of the agent, and we construct representations that formalize how the agent processes data. We apply our approach to a price-setting problem and a portfolio choice problem - two popular rational inattention models found in the literature for which simplifying assumptions have thus far been required to produce a tractable model. In contrast to prior results, which have been limited to cases that restrict the number of underlying shocks or their correlation structure, we present general solutions. In each case, we show that imposing such restrictions impacts the form and interpretation of solutions and implies suboptimal decision-making by agents.
Mechanics of linear quadratic Gaussian rational inattention tracking problems
Note: This is an previous version of the working paper Mechanics of static quadratic Gaussian rational inattention tracking problems, although it contains some sections not included there. In particular, it expands on the dynamic case and provides more detail on the equilibrium solution to the rational inattetion price-setting problem.
This paper presents a general framework for constructing and solving the multivariate static linear quadratic Gaussian (LQG) rational inattention tracking problem. We interpret the nature of the solution and the implied action of the agent, and we construct representations that formalize how the agent processes data. We apply this infrastructure to the rational inattention price-setting problem, confirming the result that a conditional response to economics shocks is possible, but casting doubt on a common assumption made in the literature. We show that multiple equilibria and a social cost of increased attention can arise in these models. We consider the extension to the dynamic problem and provide an approximate solution method that achieves low approximation error for many applications found in the LQG rational inattention literature.
Estimating time series models by state space methods in Python: Statsmodels
This paper describes an object oriented approach to the estimation of time series models using state space methods and presents an implementation in the Python programming language. This approach at once allows for fast computation, a variety of out-of-the-box features, and easy extensibility. We show how to construct a custom state space model, retrieve filtered and smoothed estimates of the unobserved state, and perform parameter estimation using classical and Bayesian methods. The mapping from theory to implementation is presented explicitly and is illustrated at each step by the development of three example models: an ARMA(1,1) model, the local level model, and a simple real business cycle macroeconomic model. Finally, four fully implemented time series models are presented: SARIMAX, VARMAX, unobserved components, and dynamic factor models. These models can immediately be applied by users.